Northern Lights at Yellowknife
Concatenation to Create or Coincidental Conundrum?
OK, I was looking at dT/dt for various countries and Canada kept coming up hot. (Well, not actually HOT hot, but warming very rapidly from darned cold to only sort of very cold ;-)
I dredged through the dT/dt file by month, looking for “odd records” to see if it was just a flakey month or broken station. I found many “odd” records, especially for Canada. (And some more in Asia, but that is for “another day”…) Often, there were many “Modification History” flags. The Mod Flag says that something changed, so the data must be “modified” in a different way from the prior data. As I understand it, it could be a new thermometer, or a different Time of Observation (even if not adjusted for that TOBS), or even two thermometers at slightly different places but counted as the same place in the StationID.
So I decided to write my “combine mod flags” code to see if that would change the results. It did, but not by much. “The Problem” still remained. It was still a case of “Blame Canada”. But for what? Why? You can’t just palm it off with “Blame Canada” and point at… at… nothing?
So I’ve spent a few days with this conundrum trying to figure out what to do with it. (Thus the odd lack of new material in postings as I’ve whacked my head against this wall…) The end result is this one very long posting. It may take reading it through a small chunk at a time… especially the long blocks of reports. But in the end, I have the conundrum. What to do with it?
I’ve decided to just point out some examples and “punt” to the world. To see if other folks have some ideas on this. Maybe someone near one of the example stations will have some insight. Has it actually warmed a bunch, suddenly? Was a new type of thermometer rolled out? Are these places having massive growth? Did they all just install heaters or “jet engine warm up aprons”? Someone must have a clue.
OK, so what IS this Conundrum?
Fort Smith – It had to be a Smith…
Here is one example, Fort Smith. I’ve decided to ‘lead off’ with the GISS graphs of the GHCN data. That way folks who must have graphs will have something to look at and folks who want to toss rocks at my methods will have to confront the GISS product first. So we’ll look at a couple of GISS graphs in rapid succession. I found these from looking at blocks of numbers ( some of which will be put down below ) and worked backwards to the graphs. (On a Mac you can “CTRL click” the graph to open it in a new window and get a bigger version. I assume on a PC it will be some kind of “right click” or…)
First off, notice that there are a very large number of Mod Flags all assembled to make this one station data series. Then notice that the basic record shows no warming trend, but after the 1987-to date series (the “6” mod flag) gets spliced on, we have a warming hockey stick. So “global warming” only chose to show up in a spike in the last few years. Strange behaviour for what is supposed to have been a gradual growth of CO2 over the length of the industrial revolution…
But it’s only one station, right?
Fort Chimo
Well, this one is rather interesting too:
Here we have a trend downward, suddenly turned into an up trend after the splice. And again we have 7 modification history flags. So much experimentation… one can only wonder why.
Are there any with fewer flags?
Fort Chipewya
This one is interesting because we again have a splice, but there are large gaps in the data. The up trend largely comes from that approximately 8 degree rise from one very low data point just after 1900 to the following more normal period ending in the hot spike in the 1930s. The best the “post slice” segment can do is preserve that 1930s era.
What to make of it?
I don’t know…
It could be simply that in the 1930’s we were in the same phase of the PDO (or some other ocean current). It is a (roughly) 60 year cycle and 1935 + 60 = 1995. And it could be just that the impact on parts of Canada are much larger than on the USA. Or it could be something more “odd”. It is also possible that each station has it’s own “issues” and all we are seeing is a set of disconnected oddities.
But there are many such “oddities”…
How Did I Stumble On These?
Ok, here’s the part some folks seem to hate. Blocks of numbers. Yes, digging for gold is hard work and you must dig through big piles of dross to find the nuggets.
That’s life.
So I was doing my “dT/dt” and got to wondering: Were there any monthly “Delta Temps” that were particularly strong? Was there a pattern to these? So I sorted the dT/dt file (basically a v2.mean file converted to “delta T” year over year) by “Delta T” in a few sample months, then looked at the top of it. What were the stations and years that had the most exotic “Delta T” for all data for that month? And I found a LOT of Canada (and some Asia).
The above charts were from the “April” sort. I also did a January, July, and October sort. There may yet be more nuggets to be found in other monthly sorts / series as well. Yet another “Dig Here!”. One of the fascinating bits about this is the years. We are not even close to “extreme weather” or extreme changes if these are indicative of past swings. Notice that the fourth temperature column is April and these are in 1/10 C, so that first record is saying that the 1978 April record was 15.6 C lower than the next earlier valid April (that is most likely 1977, but if 1977 were a missing data flag, it would be 1976, or 1975, or…)
Some samples:
From April.Station.Rank
The file format is the 12 digit StationID ( 3 digit ‘country code’, 5 digits of station, 3 of substation, one “modification history flag” digit, then a 4 digit year. A Space. Then 12 monthly ‘Delta T” fields showing how that month changed when compared to the next prior valid monthly temperature in that month. That is, the “change of Temperature” year over year (or over some prior year if intervening values were missing). But sorted by a particular month (in this case, April). Finally, it is followed by all the “meta data” for a station. Name, Latitude, longitude, altitude, etc. (Much of this data is off the right edge in the ‘pre-formatted view’ but visible in the “ragged right” view.)
The Unix commands “head” to get the top set of lines and “tail” to get the bottom set of lines, are used to sample each end of the sorted set for “extreme” values.
[chiefio@Hummer data]$ head -30 April.Station.Rank 2222321900031978 43 102 35 -156 -83 -88 -12 -24 -15 75 -51 -127 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A 2113554200031880 -12 -51 -64 -148 -48 -7 1 7 15 27 44 20 IRGIZ 48.62 61.27 114 122R -9FLxxLA-9A-9COOL DESERT A 2222321900041992 0 0 0 -148 -31 -78 0 4 3 -117 -94 82 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A 4037190600001954 -46 -1 -4 -144 0 57 -16 9 3 11 -42 51 FORT CHIMO 58.10 -68.40 36 44R -9HIxxno-9A-9WOODED TUNDRA A 4037191300101838 -34 -45 85 -144 -77 -92 15 -39 33 17 -110 -45 CHURCHILL FACTORY 58.80 -94.00 287 0R -9FLxxCO 1x-9BOGS, BOG WOODS A 2222333000021992 -45 0 80 -143 -21 -78 -8 11 -31 -88 -3 75 SALEHARD 66.53 66.67 16 30S 22FLxxno-9x-9BOGS, BOG WOODS B 4037190600011954 -46 -1 -4 -143 0 57 -16 9 3 10 -42 51 FORT CHIMO 58.10 -68.40 36 44R -9HIxxno-9A-9WOODED TUNDRA A 2222355200021998 63 -19 -27 -139 -49 -6 64 36 -62 -100 -122 56 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B 4037193400041945 -76 40 0 -139 0 1 -3 0 0 0 0 0 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A 4037193300001907 -85 -3 -34 -138 -90 -61 -42 -16 0 -27 1 33 FORT CHIPEWYA 58.77 -111.12 232 245R -9FLxxLA-9A-9SOUTH. TAIGA A 4037193300011907 -85 -3 -34 -138 -90 -61 -42 -16 0 -27 1 33 FORT CHIPEWYA 58.77 -111.12 232 245R -9FLxxLA-9A-9SOUTH. TAIGA A 2222355200021992 -38 43 60 -136 -11 -74 -12 29 -79 -48 4 76 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B 2222414300022006 -35 11 -25 -136 -3 -42 -11 20 -15 -20 -36 22 DZARDZAN 68.73 124.00 39 90R -9FLxxno-9x-9NORTH. TAIGA A 2222393300081992 -37 0 61 -135 -34 -98 -13 13 -41 -40 -11 67 HANTY-MANSIJS 61.02 69.03 46 90S 25FLxxno-9x-9MAIN TAIGA C 2222314600041992 -21 54 68 -134 -5 -43 -34 0 -69 -85 -63 0 MYS KAMENNYJ 68.47 73.58 8 8R -9FLxxCO 1A-9WATER B 4037193400021945 0 36 54 -134 -51 -6 6 11 -19 -17 -114 -82 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A 4037190100101954 -41 29 17 -132 8 47 -2 11 15 12 -51 58 INDIAN HOUSE LAKE,QU 56.23 -64.73 311 415R -9MVxxLA-9x-9WOODED TUNDRA A 2222517300011946 -15 -51 2 -131 -20 7 -1 16 -31 29 23 -9 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B 4037193500001945 -68 24 29 -131 0 -8 -4 18 -19 -19 -125 -132 HAY RIVER,N.W 60.83 -115.78 166 173R -9FLMAno-9x-9SOUTH. TAIGA C 4037193500021945 -68 24 29 -131 0 -8 -4 18 -19 -19 -125 -132 HAY RIVER,N.W 60.83 -115.78 166 173R -9FLMAno-9x-9SOUTH. TAIGA C 2222321900001978 46 98 33 -129 -83 -88 -18 -23 -16 76 -47 -127 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A 2222321900011978 45 98 33 -129 -83 -88 -18 0 -6 76 -47 -127 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A 2222307400002006 -18 20 -34 -128 -20 58 53 1 29 -35 -2 31 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C 2223512100041996 -51 57 -86 -128 -4 -9 3 -12 -11 -35 -12 -3 ORENBURG 51.68 55.10 117 145U 459FLxxno-9x-9COOL GRASS/SHRUBB 4037193600001945 -51 41 46 -128 -47 -7 5 14 -12 0 -56 -83 YELLOWKNIFE,N 62.47 -114.45 206 188R -9FLxxLA-9A-9WATER C 4257016200001949 44 -18 78 -128 -6 4 -3 47 55 35 76 74 UMIAT 69.37 -152.13 85 171R -9FLMAno-9A-9WOODED TUNDRA A 2222321900021978 40 94 30 -127 -83 -84 -14 -32 -5 77 -49 -131 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A 2222517300061998 -17 -36 -73 -127 -68 -19 0 -41 -5 24 -24 73 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B 4037190700011954 -55 14 -14 -127 28 28 44 37 17 9 -29 58 INUKJUAK, QUE 58.45 -78.12 6 19R -9HIxxCO 1x-9WATER A 4037190700021954 -55 14 -14 -127 27 28 44 37 17 8 -29 58 INUKJUAK, QUE 58.45 -78.12 6 19R -9HIxxCO 1x-9WATER A [chiefio@Hummer data]$
and, as ‘ragged right’ so folks can see all the meta-data, though ugly:
[chiefio@Hummer data]$ head -30 April.Station.Rank
2222321900031978 43 102 35 -156 -83 -88 -12 -24 -15 75 -51 -127 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A
2113554200031880 -12 -51 -64 -148 -48 -7 1 7 15 27 44 20 IRGIZ 48.62 61.27 114 122R -9FLxxLA-9A-9COOL DESERT A
2222321900041992 0 0 0 -148 -31 -78 0 4 3 -117 -94 82 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A
4037190600001954 -46 -1 -4 -144 0 57 -16 9 3 11 -42 51 FORT CHIMO 58.10 -68.40 36 44R -9HIxxno-9A-9WOODED TUNDRA A
4037191300101838 -34 -45 85 -144 -77 -92 15 -39 33 17 -110 -45 CHURCHILL FACTORY 58.80 -94.00 287 0R -9FLxxCO 1x-9BOGS, BOG WOODS A
2222333000021992 -45 0 80 -143 -21 -78 -8 11 -31 -88 -3 75 SALEHARD 66.53 66.67 16 30S 22FLxxno-9x-9BOGS, BOG WOODS B
4037190600011954 -46 -1 -4 -143 0 57 -16 9 3 10 -42 51 FORT CHIMO 58.10 -68.40 36 44R -9HIxxno-9A-9WOODED TUNDRA A
2222355200021998 63 -19 -27 -139 -49 -6 64 36 -62 -100 -122 56 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B
4037193400041945 -76 40 0 -139 0 1 -3 0 0 0 0 0 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A
4037193300001907 -85 -3 -34 -138 -90 -61 -42 -16 0 -27 1 33 FORT CHIPEWYA 58.77 -111.12 232 245R -9FLxxLA-9A-9SOUTH. TAIGA A
4037193300011907 -85 -3 -34 -138 -90 -61 -42 -16 0 -27 1 33 FORT CHIPEWYA 58.77 -111.12 232 245R -9FLxxLA-9A-9SOUTH. TAIGA A
2222355200021992 -38 43 60 -136 -11 -74 -12 29 -79 -48 4 76 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B
2222414300022006 -35 11 -25 -136 -3 -42 -11 20 -15 -20 -36 22 DZARDZAN 68.73 124.00 39 90R -9FLxxno-9x-9NORTH. TAIGA A
2222393300081992 -37 0 61 -135 -34 -98 -13 13 -41 -40 -11 67 HANTY-MANSIJS 61.02 69.03 46 90S 25FLxxno-9x-9MAIN TAIGA C
2222314600041992 -21 54 68 -134 -5 -43 -34 0 -69 -85 -63 0 MYS KAMENNYJ 68.47 73.58 8 8R -9FLxxCO 1A-9WATER B
4037193400021945 0 36 54 -134 -51 -6 6 11 -19 -17 -114 -82 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A
4037190100101954 -41 29 17 -132 8 47 -2 11 15 12 -51 58 INDIAN HOUSE LAKE,QU 56.23 -64.73 311 415R -9MVxxLA-9x-9WOODED TUNDRA A
2222517300011946 -15 -51 2 -131 -20 7 -1 16 -31 29 23 -9 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B
4037193500001945 -68 24 29 -131 0 -8 -4 18 -19 -19 -125 -132 HAY RIVER,N.W 60.83 -115.78 166 173R -9FLMAno-9x-9SOUTH. TAIGA C
4037193500021945 -68 24 29 -131 0 -8 -4 18 -19 -19 -125 -132 HAY RIVER,N.W 60.83 -115.78 166 173R -9FLMAno-9x-9SOUTH. TAIGA C
2222321900001978 46 98 33 -129 -83 -88 -18 -23 -16 76 -47 -127 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A
2222321900011978 45 98 33 -129 -83 -88 -18 0 -6 76 -47 -127 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A
2222307400002006 -18 20 -34 -128 -20 58 53 1 29 -35 -2 31 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C
2223512100041996 -51 57 -86 -128 -4 -9 3 -12 -11 -35 -12 -3 ORENBURG 51.68 55.10 117 145U 459FLxxno-9x-9COOL GRASS/SHRUBB
4037193600001945 -51 41 46 -128 -47 -7 5 14 -12 0 -56 -83 YELLOWKNIFE,N 62.47 -114.45 206 188R -9FLxxLA-9A-9WATER C
4257016200001949 44 -18 78 -128 -6 4 -3 47 55 35 76 74 UMIAT 69.37 -152.13 85 171R -9FLMAno-9A-9WOODED TUNDRA A
2222321900021978 40 94 30 -127 -83 -84 -14 -32 -5 77 -49 -131 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A
2222517300061998 -17 -36 -73 -127 -68 -19 0 -41 -5 24 -24 73 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B
4037190700011954 -55 14 -14 -127 28 28 44 37 17 9 -29 58 INUKJUAK, QUE 58.45 -78.12 6 19R -9HIxxCO 1x-9WATER A
4037190700021954 -55 14 -14 -127 27 28 44 37 17 8 -29 58 INUKJUAK, QUE 58.45 -78.12 6 19R -9HIxxCO 1x-9WATER A
[chiefio@Hummer data]$
Oddly enough (or maybe not so odd, as it’s really just finding volatility, not direction, at high latitudes) looking at the ‘tail’ of the file finds many of the same stations as having had oddly “hot” Aprils in some years, though with a bit more Asia:
[chiefio@Hummer data]$ tail -20 April.Station.Rank 4037106600101949 -61 -22 72 124 -28 -31 -15 -1 2 -43 73 -20 KEG RIVER,AL 57.78 -117.87 427 470R -9MVxxno-9x-9MAIN TAIGA A 4037193400001949 -39 -31 74 124 -47 -27 -20 6 -5 -57 35 -9 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A 2222555100021945 35 -9 -21 125 16 6 -1 -10 -10 -33 18 8 MARKOVO 64.68 170.42 26 60R -9FLMAno-9A-9NORTH. TAIGA A 4037193400021949 -40 -31 74 125 -48 -27 -20 5 -5 -57 36 -9 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A 2222517300061997 -52 0 0 127 0 0 0 22 3 6 0 -79 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B 7008905500011991 0 -31 0 127 0 0 0 0 0 0 0 0 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 7008905500031991 0 -31 0 127 0 0 0 0 0 0 0 0 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 2222355200021997 -25 -59 23 130 34 0 -74 15 56 25 -62 -74 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B 2222555100011945 31 -10 -32 131 19 0 -6 -5 -11 -30 18 9 MARKOVO 64.68 170.42 26 60R -9FLMAno-9A-9NORTH. TAIGA A 2222426600021897 -38 14 34 133 63 0 21 0 2 -46 -22 102 VERHOJANSK 67.55 133.38 137 270R -9MVMAno-9x-9NORTH. TAIGA A 4037113000201949 -39 -34 45 133 7 -18 -20 16 -35 -55 45 -33 MELFORT,SA 52.87 -104.60 463 459R -9HIxxno-9x-9COOL FIELD/WOODSC 2222410500001990 -18 -62 47 135 0 0 0 0 0 0 0 0 ESSEJ 68.47 102.37 271 307R -9FLxxLA-9x-9NORTH. TAIGA A 2222067400061995 0 0 0 140 -4 -16 36 47 21 -24 -5 0 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A 2222355200022007 245 -98 8 141 -21 -32 29 4 -5 73 90 10 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B 2222412500052007 61 -116 19 141 -15 18 -34 -2 -13 56 29 0 OLENEK 68.50 112.43 220 262R -9MVxxno-9x-9NORTH. TAIGA A 2222089100001943 -67 78 111 144 2 16 -8 25 -6 33 17 -137 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100021943 -67 78 110 145 2 17 -8 25 -6 33 17 -137 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100011943 -67 75 111 148 5 17 -9 25 -9 33 26 -139 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100062007 33 -152 2 152 0 4 -37 8 11 68 -24 -60 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222307400002007 194 -122 6 156 -23 10 -10 4 10 69 47 -34 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C [chiefio@Hummer data]$
And as “ragged right”:
[chiefio@Hummer data]$ tail -20 April.Station.Rank
4037106600101949 -61 -22 72 124 -28 -31 -15 -1 2 -43 73 -20 KEG RIVER,AL 57.78 -117.87 427 470R -9MVxxno-9x-9MAIN TAIGA A
4037193400001949 -39 -31 74 124 -47 -27 -20 6 -5 -57 35 -9 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A
2222555100021945 35 -9 -21 125 16 6 -1 -10 -10 -33 18 8 MARKOVO 64.68 170.42 26 60R -9FLMAno-9A-9NORTH. TAIGA A
4037193400021949 -40 -31 74 125 -48 -27 -20 5 -5 -57 36 -9 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A
2222517300061997 -52 0 0 127 0 0 0 22 3 6 0 -79 MYS SMIDTA 68.90 -179.37 4 0R -9FLxxCO 1x-9TUNDRA B
7008905500011991 0 -31 0 127 0 0 0 0 0 0 0 0 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
7008905500031991 0 -31 0 127 0 0 0 0 0 0 0 0 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
2222355200021997 -25 -59 23 130 34 0 -74 15 56 25 -62 -74 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B
2222555100011945 31 -10 -32 131 19 0 -6 -5 -11 -30 18 9 MARKOVO 64.68 170.42 26 60R -9FLMAno-9A-9NORTH. TAIGA A
2222426600021897 -38 14 34 133 63 0 21 0 2 -46 -22 102 VERHOJANSK 67.55 133.38 137 270R -9MVMAno-9x-9NORTH. TAIGA A
4037113000201949 -39 -34 45 133 7 -18 -20 16 -35 -55 45 -33 MELFORT,SA 52.87 -104.60 463 459R -9HIxxno-9x-9COOL FIELD/WOODSC
2222410500001990 -18 -62 47 135 0 0 0 0 0 0 0 0 ESSEJ 68.47 102.37 271 307R -9FLxxLA-9x-9NORTH. TAIGA A
2222067400061995 0 0 0 140 -4 -16 36 47 21 -24 -5 0 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A
2222355200022007 245 -98 8 141 -21 -32 29 4 -5 73 90 10 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B
2222412500052007 61 -116 19 141 -15 18 -34 -2 -13 56 29 0 OLENEK 68.50 112.43 220 262R -9MVxxno-9x-9NORTH. TAIGA A
2222089100001943 -67 78 111 144 2 16 -8 25 -6 33 17 -137 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100021943 -67 78 110 145 2 17 -8 25 -6 33 17 -137 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100011943 -67 75 111 148 5 17 -9 25 -9 33 26 -139 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100062007 33 -152 2 152 0 4 -37 8 11 68 -24 -60 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222307400002007 194 -122 6 156 -23 10 -10 4 10 69 47 -34 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C
[chiefio@Hummer data]$
So what have we learned? Perhaps only that high latitudes have high volatility and are much more sensitive to swings of ocean cycles and normal temperature events than the equatorial bands (something that ought to have been obvious, given the stability of the equator with respect to solar heating).
But this is important to see in the data. It says that if you have “exceptional increases in warmth” in the far northern latitudes, it really is not at all unusual. Neither are exceptional decreases in warmth. The “1990’s and 2000’s” were in many ways just a “do over” of the 1930’s warmth and 1940’s volatility. And given that the globe is strongly biased to lots of land area very far north in the Northern Hemisphere, we can reasonably expect that any land data series even if it is ‘area adjusted’ will have a bias toward influence by these volatile areas as they respond to the ocean cycles. If you do not control for that volatility and sensitivity, you will be subject to running to and fro as the ocean currents shift.
A corollary to that would be: Looking at the equatorial and island / oceanic bands ought to give a more stable indication of the net change of the planet, not biased by a volatile and sensitive sample of northern land area. (And folks who have been reading this site for a while will remember that when we looked at the “Pacific Basin” we found no warming trend.)
So what do the other months sorts look like? I’ll just put some samples of the data here. Folks who want to go exploring can visit the GISS web site and see if any of these are “interesting” in some way.
January Station Rank, both “heads” and “tails”
Does anything interesting show up in the dead of winter?
[chiefio@Hummer data]$ head -20 Jan.Station.Rank 4037196500101982 -338 -82 -111 5 -50 21 17 -1 11 -50 -95 19 MAYO A,YT 63.62 -135.87 504 712R -9MVxxno-9A-9WOODED TUNDRA A 4037196600001982 -295 -79 -92 9 -45 24 16 -2 17 -60 -93 12 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 4037196400701982 -294 -75 -82 5 -43 22 2 0 22 -50 -91 43 BRAEBURN,YT 61.47 -135.78 716 1127R -9MVxxno-9A-9MAIN TAIGA A 4037194900001982 -283 -54 -94 13 -43 29 11 -11 22 -25 -88 56 FARO, Y.T. 62.23 -133.35 717 1016R -9MVxxno-9A-9MAIN TAIGA B 4037194900201982 -274 -74 0 16 -43 29 19 -7 14 -44 -94 40 CARMACKS,YT 62.10 -136.30 525 683R -9MVxxno-9x-9MAIN TAIGA B 4037196400001982 -272 -67 -73 -1 -35 23 6 -14 22 -37 -65 40 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C 4037196400021982 -272 -67 -73 -1 -35 23 6 -14 22 -37 -65 40 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C 4037196400011982 -271 -67 -72 -1 -35 22 6 -14 21 -37 -65 40 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C 4037196400601982 -266 -100 -98 -13 -36 16 8 -15 15 -38 -44 26 BURWASH A,YT 61.37 -139.05 799 989R -9MVxxLA-9A-9ICE A 4037193500101982 -262 -42 -99 0 -39 -12 -20 -51 2 0 -103 -25 HAY RIVER PARADISE GDNS,NW 60.65 -116.00 213 206R -9FLxxno-9x-9SOUTH. TAIGA A 4257029100201982 -262 -65 -85 5 -49 0 20 1 29 -56 -46 -5 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A 4257027100011982 -260 -71 -34 11 -33 14 12 5 30 -44 -20 38 GULKANA/INTL. 62.15 -145.45 481 637R -9FLxxno-9A-9TUNDRA A 4257027100001982 -259 -72 -35 11 -32 14 12 6 30 -44 -21 37 GULKANA/INTL. 62.15 -145.45 481 637R -9FLxxno-9A-9TUNDRA A 4037196400401982 -257 -67 -66 -7 -35 17 -1 -12 19 -43 -59 41 HAINES JUNCTION,YT 60.77 -137.58 599 1370R -9MVxxno-9A-9TUNDRA A 4037104500101982 -252 -58 -56 -1 -27 24 8 -5 14 -19 -51 33 ATLIN,BC 59.57 -133.70 674 728R -9MVxxLA-9A-9TUNDRA B 4037196400501982 -250 -92 -74 -9 -39 21 0 -12 18 0 0 0 KLUANE LAKE,YT 61.02 -138.40 786 1038R -9MVxxLA-9A-9TUNDRA A 4037194500201982 -248 -60 -69 5 -28 34 16 -35 15 15 -56 28 MUNCHO LAKE,BC 58.92 -125.77 835 1241R -9MVxxno-9x-9NORTH. TAIGA A 4037193500021982 -245 -40 -74 39 -41 2 -23 -49 9 19 -86 -32 HAY RIVER,N.W 60.83 -115.78 166 173R -9FLMAno-9x-9SOUTH. TAIGA C 4037194300901982 -239 -92 -78 0 -29 41 4 -58 7 16 -56 10 WONOWON,BC 56.73 -121.80 914 856R -9HIxxno-9x-9COOL CONIFER A 4037193400001982 -236 -55 -92 22 -25 -4 -8 -58 2 34 -102 -30 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A [chiefio@Hummer data]$ tail -20 Jan.Station.Rank 4037196600001981 214 -13 50 -57 11 -33 -5 5 12 -25 9 170 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 4037187200301919 215 5 -60 19 30 -1 8 17 6 -80 -82 0 JENNER,AL 50.73 -111.18 762 758R -9HIxxno-9x-9COOL FIELD/WOODSA 4257017400101926 218 50 65 23 -20 6 14 0 -3 -13 12 -22 ALLAKAKET 66.57 -152.67 183 268R -9FLMAno-9A-9NORTH. TAIGA A 4037106800501926 221 178 73 -21 -6 -17 -48 -15 -17 52 -1 -27 PEACE RIVER CROSSING,AL 56.25 -117.25 373 464R -9HIxxno-9x-9COOL CONIFER C 4037196400301985 229 0 0 0 -3 0 17 0 0 6 0 0 CARCROSS,YT 60.18 -134.70 660 980R -9MVxxLA-9A-9TUNDRA B 2222923100072007 230 -10 -10 107 1 -61 12 19 -3 25 29 -46 KOLPASEVO 58.32 82.95 75 60S 25FLxxno-9x-9COOL MIXED C 2222347200052007 235 -83 3 105 -14 -19 3 7 -8 49 95 -9 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A 2222494400031930 242 0 13 -5 -20 16 21 -2 13 20 10 70 OLEKMINSK 60.40 120.42 226 237S 29HIxxno-9A 2MAIN TAIGA B 2222355200022007 245 -98 8 141 -21 -32 29 4 -5 73 90 10 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B 4037196600001926 245 91 72 23 -4 -9 3 2 -5 -20 -31 -41 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 4037196600011926 246 91 73 24 -3 -7 2 3 -5 -20 -32 -41 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 2222395500022007 247 -56 0 122 0 -57 24 26 -13 43 58 -28 ALEKSANDROVSK 60.43 77.87 48 90R -9FLxxno-9A-9MAIN TAIGA C 4257029100201926 250 71 85 7 -2 -8 -2 3 -9 -14 24 -29 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A 4257029100211926 250 72 85 7 -2 -8 -2 2 -10 -13 25 -30 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A 4257029100221926 250 72 85 7 -2 -8 -2 2 -10 -13 25 -30 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A 2222388400072007 258 -27 -6 99 0 -52 4 19 -24 44 73 -1 BOR 61.60 90.02 58 113R -9FLxxno-9A-9MAIN TAIGA B 4037196600301926 258 95 72 25 -4 -5 -1 10 -3 -19 -36 -44 SWEDE CREEK,YT 64.10 -139.75 320 930R -9MVxxno-9x-9WOODED TUNDRA A 4257026400201926 261 74 99 43 18 32 32 26 24 -28 29 -39 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A 4037187400021951 272 -28 -49 7 17 -33 5 -39 -32 -32 36 -87 LETHBRIDGE,AL 49.63 -112.80 929 916U 54HIxxno-9A 5COOL CROPS B 4037194900301985 294 -100 0 0 0 0 17 0 0 0 0 0 DRURY CREEK,YT 62.20 -134.38 609 1158R -9MVxxLA-9x-9MAIN TAIGA A [chiefio@Hummer data]$
And as “ragged right” (Someday I’ll find out if I can swap theme to another one that lets the ‘pre formated’ tables have a scroll bar ;-)
[chiefio@Hummer data]$ head -20 Jan.Station.Rank
4037196500101982 -338 -82 -111 5 -50 21 17 -1 11 -50 -95 19 MAYO A,YT 63.62 -135.87 504 712R -9MVxxno-9A-9WOODED TUNDRA A
4037196600001982 -295 -79 -92 9 -45 24 16 -2 17 -60 -93 12 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A
4037196400701982 -294 -75 -82 5 -43 22 2 0 22 -50 -91 43 BRAEBURN,YT 61.47 -135.78 716 1127R -9MVxxno-9A-9MAIN TAIGA A
4037194900001982 -283 -54 -94 13 -43 29 11 -11 22 -25 -88 56 FARO, Y.T. 62.23 -133.35 717 1016R -9MVxxno-9A-9MAIN TAIGA B
4037194900201982 -274 -74 0 16 -43 29 19 -7 14 -44 -94 40 CARMACKS,YT 62.10 -136.30 525 683R -9MVxxno-9x-9MAIN TAIGA B
4037196400001982 -272 -67 -73 -1 -35 23 6 -14 22 -37 -65 40 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C
4037196400021982 -272 -67 -73 -1 -35 23 6 -14 22 -37 -65 40 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C
4037196400011982 -271 -67 -72 -1 -35 22 6 -14 21 -37 -65 40 WITHEHORSE, Y 60.72 -135.07 703 947S 15MVxxno-9x-9TUNDRA C
4037196400601982 -266 -100 -98 -13 -36 16 8 -15 15 -38 -44 26 BURWASH A,YT 61.37 -139.05 799 989R -9MVxxLA-9A-9ICE A
4037193500101982 -262 -42 -99 0 -39 -12 -20 -51 2 0 -103 -25 HAY RIVER PARADISE GDNS,NW 60.65 -116.00 213 206R -9FLxxno-9x-9SOUTH. TAIGA A
4257029100201982 -262 -65 -85 5 -49 0 20 1 29 -56 -46 -5 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A
4257027100011982 -260 -71 -34 11 -33 14 12 5 30 -44 -20 38 GULKANA/INTL. 62.15 -145.45 481 637R -9FLxxno-9A-9TUNDRA A
4257027100001982 -259 -72 -35 11 -32 14 12 6 30 -44 -21 37 GULKANA/INTL. 62.15 -145.45 481 637R -9FLxxno-9A-9TUNDRA A
4037196400401982 -257 -67 -66 -7 -35 17 -1 -12 19 -43 -59 41 HAINES JUNCTION,YT 60.77 -137.58 599 1370R -9MVxxno-9A-9TUNDRA A
4037104500101982 -252 -58 -56 -1 -27 24 8 -5 14 -19 -51 33 ATLIN,BC 59.57 -133.70 674 728R -9MVxxLA-9A-9TUNDRA B
4037196400501982 -250 -92 -74 -9 -39 21 0 -12 18 0 0 0 KLUANE LAKE,YT 61.02 -138.40 786 1038R -9MVxxLA-9A-9TUNDRA A
4037194500201982 -248 -60 -69 5 -28 34 16 -35 15 15 -56 28 MUNCHO LAKE,BC 58.92 -125.77 835 1241R -9MVxxno-9x-9NORTH. TAIGA A
4037193500021982 -245 -40 -74 39 -41 2 -23 -49 9 19 -86 -32 HAY RIVER,N.W 60.83 -115.78 166 173R -9FLMAno-9x-9SOUTH. TAIGA C
4037194300901982 -239 -92 -78 0 -29 41 4 -58 7 16 -56 10 WONOWON,BC 56.73 -121.80 914 856R -9HIxxno-9x-9COOL CONIFER A
4037193400001982 -236 -55 -92 22 -25 -4 -8 -58 2 34 -102 -30 FORT SMITH 60.00 -112.00 203 192R -9FLxxno-9A-9SOUTH. TAIGA A
[chiefio@Hummer data]$ tail -20 Jan.Station.Rank
4037196600001981 214 -13 50 -57 11 -33 -5 5 12 -25 9 170 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A
4037187200301919 215 5 -60 19 30 -1 8 17 6 -80 -82 0 JENNER,AL 50.73 -111.18 762 758R -9HIxxno-9x-9COOL FIELD/WOODSA
4257017400101926 218 50 65 23 -20 6 14 0 -3 -13 12 -22 ALLAKAKET 66.57 -152.67 183 268R -9FLMAno-9A-9NORTH. TAIGA A
4037106800501926 221 178 73 -21 -6 -17 -48 -15 -17 52 -1 -27 PEACE RIVER CROSSING,AL 56.25 -117.25 373 464R -9HIxxno-9x-9COOL CONIFER C
4037196400301985 229 0 0 0 -3 0 17 0 0 6 0 0 CARCROSS,YT 60.18 -134.70 660 980R -9MVxxLA-9A-9TUNDRA B
2222923100072007 230 -10 -10 107 1 -61 12 19 -3 25 29 -46 KOLPASEVO 58.32 82.95 75 60S 25FLxxno-9x-9COOL MIXED C
2222347200052007 235 -83 3 105 -14 -19 3 7 -8 49 95 -9 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A
2222494400031930 242 0 13 -5 -20 16 21 -2 13 20 10 70 OLEKMINSK 60.40 120.42 226 237S 29HIxxno-9A 2MAIN TAIGA B
2222355200022007 245 -98 8 141 -21 -32 29 4 -5 73 90 10 TARKO-SALE 64.92 77.82 27 82R -9FLxxno-9A-9NORTH. TAIGA B
4037196600001926 245 91 72 23 -4 -9 3 2 -5 -20 -31 -41 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A
4037196600011926 246 91 73 24 -3 -7 2 3 -5 -20 -32 -41 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A
2222395500022007 247 -56 0 122 0 -57 24 26 -13 43 58 -28 ALEKSANDROVSK 60.43 77.87 48 90R -9FLxxno-9A-9MAIN TAIGA C
4257029100201926 250 71 85 7 -2 -8 -2 3 -9 -14 24 -29 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A
4257029100211926 250 72 85 7 -2 -8 -2 2 -10 -13 25 -30 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A
4257029100221926 250 72 85 7 -2 -8 -2 2 -10 -13 25 -30 EAGLE 64.79 -141.20 259 518R -9MVxxno-9A-9NORTH. TAIGA A
2222388400072007 258 -27 -6 99 0 -52 4 19 -24 44 73 -1 BOR 61.60 90.02 58 113R -9FLxxno-9A-9MAIN TAIGA B
4037196600301926 258 95 72 25 -4 -5 -1 10 -3 -19 -36 -44 SWEDE CREEK,YT 64.10 -139.75 320 930R -9MVxxno-9x-9WOODED TUNDRA A
4257026400201926 261 74 99 43 18 32 32 26 24 -28 29 -39 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A
4037187400021951 272 -28 -49 7 17 -33 5 -39 -32 -32 36 -87 LETHBRIDGE,AL 49.63 -112.80 929 916U 54HIxxno-9A 5COOL CROPS B
4037194900301985 294 -100 0 0 0 0 17 0 0 0 0 0 DRURY CREEK,YT 62.20 -134.38 609 1158R -9MVxxLA-9x-9MAIN TAIGA A
[chiefio@Hummer data]$
October Station Rank, both “heads” and “tails”
A bit more USA starts to show up and even a bit of Europe as that 614 in the ‘warming’ tail. Country Code 614 is Finland, so continues to fit the “high latitude” theme.
[chiefio@Hummer data]$ head -30 Oct.Station.Rank 2222004600021987 26 -16 -40 34 9 -10 -12 -8 -35 -167 -47 -12 GMO IM.E.T. 80.62 58.05 20 0R -9HIxxCO 1x-9WATER A 2154422500012000 -75 -74 10 1 -8 20 -4 8 17 -154 -79 0 TOSONTSENGEL 48.73 98.28 1723 2062R -9MVxxno-9x-9COOL DESERT A 2222089100061998 -8 0 12 -105 -55 -5 0 44 -66 -133 -10 0 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2154434100001987 -19 0 -46 59 0 0 28 8 10 -130 -28 20 MANDALGOVI 45.77 106.28 1393 1228S 10FLxxno-9x-9WARM GRASS/SHRUBC 2222067400001968 60 62 13 -27 17 -14 -28 -5 -17 -130 -177 -137 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A 4257275500501925 30 -38 -8 23 13 3 -13 13 10 -128 -5 0 FOSSTON 47.57 -95.73 399 398R -9FLxxno-9x-9WARM MIXED B 4037104300051996 -62 1 44 58 59 -20 21 -18 -40 -127 13 62 NORMAN WELLS, 65.28 -126.80 74 94R -9HIxxno-9A-9MAIN TAIGA B 4257026400201924 -1 -66 0 -93 2 0 0 0 -14 -127 23 16 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A 2222067400061998 50 -63 0 -62 -25 2 0 7 -66 -126 -35 6 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A 4257453000501913 -19 -21 31 -5 -19 44 44 3 -47 -126 9 -63 EADS 2S 38.48 -102.78 1283 1275R -9FLxxno-9x-9COOL GRASS/SHRUBB 7008905500032002 22 9 51 61 -84 -44 10 -7 -9 -124 -30 -15 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 2154427200032000 -2 -60 31 5 -6 12 -14 -1 -9 -120 -68 0 ULIASTAI 47.75 96.85 1759 2525S 15MVxxno-9x-9TUNDRA A 4257223300301885 0 0 0 0 -21 6 -32 45 -23 -119 0 0 AMITE 30.70 -90.53 51 32R -9FLxxno-9x-9WARM FOR./FIELD A 4257258200301908 0 39 27 22 -27 -13 -1 -17 -56 -119 -11 -4 WELLS 41.12 -114.97 1722 1755R -9MVDEno-9x-9COOL IRRIGATED C 4257002600041988 14 33 -6 35 -3 -4 -1 -17 -1 -118 -46 -2 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C 2222321900041992 0 0 0 -148 -31 -78 0 4 3 -117 -94 82 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A 4257002600011988 15 31 -6 35 -2 -3 0 -17 1 -117 -43 -2 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C 4257002600031988 15 31 -6 35 -2 -3 0 -17 1 -117 -43 -2 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C 4037196600011939 55 44 -35 -17 -4 17 6 -14 -50 -116 12 102 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 4257002600001988 14 31 -6 34 -2 -3 0 -17 0 -116 -46 -3 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C 4257275000101925 30 -34 -4 25 8 -6 -12 6 9 -116 -20 36 PINE RIVER DAM 46.67 -94.12 381 375R -9FLxxLA-9x-9COOL FIELD/WOODSB 2154420300012000 -70 -20 68 -1 -13 38 -14 11 19 -115 -60 0 RINCHINLHUMBE 51.12 99.67 1583 1720R -9MVxxno-9x-9SOUTH. TAIGA C 7008903400101962 -1 -12 -26 66 -129 48 68 19 1 -115 -48 0 BELGRANO -77.90 -34.50 55 0R -9FLICCO 1x-9ANTARCTICA A 4037196600001939 56 44 -36 -16 -4 16 7 -14 -51 -114 12 103 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A 4257264400101925 36 7 36 41 23 10 8 6 47 -114 -6 41 ALBERT LEA 3SE 43.62 -93.42 374 383S 18FLxxLA-9x-9COOL CROPS B 2222004600001963 2 -36 -88 -8 0 2 0 10 -10 -113 -130 -25 GMO IM.E.T. 80.62 58.05 20 0R -9HIxxCO 1x-9WATER A 4257265300401925 34 9 55 54 35 30 14 39 47 -113 -15 48 GANN VALLEY 4NW 44.07 -99.07 524 515R -9FLxxno-9x-9COOL CROPS A 4257265400401925 14 -6 44 43 27 10 0 22 36 -113 -4 45 WATERTOWN FAA AP 44.92 -97.15 532 529S 18FLxxLA-9A 2COOL CROPS C 2222333000021998 21 0 -3 -98 -34 0 70 21 -56 -112 -88 26 SALEHARD 66.53 66.67 16 30S 22FLxxno-9x-9BOGS, BOG WOODS B 4037149600101974 -23 -6 -28 -31 -45 -20 -18 -29 -60 -112 13 6 CONTWOYTO LAKE,NW 65.48 -110.37 451 439R -9FLxxLA-9x-9TUNDRA A [chiefio@Hummer data]$ tail -30 Oct.Station.Rank 2222027400001967 -30 -20 72 17 17 0 1 -6 6 111 73 -57 OSTROV UEDINE 77.50 82.20 23 0R -9FLxxCO 1x-9WATER A 2222347200011978 40 81 21 -34 -14 -24 27 0 -3 111 -10 1 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A 2222347200041978 40 81 21 -34 -14 -24 27 1 -3 111 -10 1 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A 4257024900101957 156 19 50 18 17 47 -7 12 3 111 144 43 FAREWELL FAA AP 62.52 -153.89 457 417R -9FLxxno-9A-9TUNDRA A 6140283600001961 78 62 -4 -39 -49 16 -19 -9 -3 111 63 -38 SODANKYLA 67.37 26.65 179 191R -9FLFOno-9A-9MAIN TAIGA A 6140283600011961 78 64 -1 -40 -50 15 -20 -10 -3 111 64 -39 SODANKYLA 67.37 26.65 179 191R -9FLFOno-9A-9MAIN TAIGA A 2222347200031978 39 81 20 -34 -16 -22 33 5 -2 112 -8 3 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A 4037186300101920 0 61 31 -64 -5 -16 7 15 8 113 78 -17 YELLOW GRASS,SA 49.82 -104.18 580 575R -9FLxxno-9x-9COOL GRASS/SHRUBA 4257256600501926 11 -8 -13 -19 12 0 7 29 -10 113 -3 2 HARRISON 42.68 -103.88 1478 1468R -9HIxxno-9x-9COOL IRRIGATED A 2222307400001967 5 105 85 83 -4 21 53 9 -14 115 97 33 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C 2222307400031967 5 105 85 83 -4 41 53 9 -14 115 97 33 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C 2222347200001890 -55 -92 12 3 4 -16 -11 3 -33 115 -110 -28 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A 2154434100001989 -27 14 61 0 33 0 0 -15 13 116 0 0 MANDALGOVI 45.77 106.28 1393 1228S 10FLxxno-9x-9WARM GRASS/SHRUBC 2154434100021989 -27 0 61 0 33 0 0 -15 13 116 0 0 MANDALGOVI 45.77 106.28 1393 1228S 10FLxxno-9x-9WARM GRASS/SHRUBC 2084085600022001 0 0 0 0 0 0 0 0 0 117 4 0 ZAHEDAN 29.47 60.88 1370 1580U 93HIxxno-9x-9WARM FIELD WOODSC 2222089100001967 39 89 85 47 5 24 73 -32 9 118 109 48 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100011967 41 87 80 45 4 27 42 -4 11 118 110 -12 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100021967 39 89 85 47 5 24 46 -7 9 118 130 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100031967 40 89 84 70 5 24 47 -6 9 118 109 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100041967 40 89 84 70 5 24 47 -6 9 118 109 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 2222089100051967 39 89 84 47 5 24 47 -6 9 118 109 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A 4037191100201973 26 30 -5 11 54 75 39 49 55 118 69 43 GLADMAN POINT A,NW 68.67 -97.80 25 9R -9FLxxCO 1A-9TUNDRA A 4037191100001973 51 27 8 -17 51 80 58 49 48 122 54 42 SHEPHERD BAY, 68.82 -93.43 42 48R -9FLxxCO 8A-9TUNDRA A 4257026400201925 -90 -22 -41 85 5 -1 -28 -27 30 125 -24 102 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A 2154423900001989 -1 9 58 0 19 0 9 0 0 129 -7 0 BULGAN 48.80 103.55 1208 1484S 15HIxxno-9x-9COOL GRASS/SHRUBA 2154423900021989 -1 9 58 0 19 0 9 0 0 129 0 0 BULGAN 48.80 103.55 1208 1484S 15HIxxno-9x-9COOL GRASS/SHRUBA 2222307400001978 24 59 29 -54 -24 -31 31 6 -9 130 -28 -40 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C 2222314600021978 12 68 46 -103 0 -42 -25 -30 -7 131 -53 -42 MYS KAMENNYJ 68.47 73.58 8 8R -9FLxxCO 1A-9WATER B 2222314600031978 12 68 46 -103 0 -42 -25 -30 -7 131 -53 -42 MYS KAMENNYJ 68.47 73.58 8 8R -9FLxxCO 1A-9WATER B 2222067400001967 -27 48 82 55 8 11 8 7 3 173 94 -29 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A [chiefio@Hummer data]$
And the Ragged Right listing:
[chiefio@Hummer data]$ head -30 Oct.Station.Rank
2222004600021987 26 -16 -40 34 9 -10 -12 -8 -35 -167 -47 -12 GMO IM.E.T. 80.62 58.05 20 0R -9HIxxCO 1x-9WATER A
2154422500012000 -75 -74 10 1 -8 20 -4 8 17 -154 -79 0 TOSONTSENGEL 48.73 98.28 1723 2062R -9MVxxno-9x-9COOL DESERT A
2222089100061998 -8 0 12 -105 -55 -5 0 44 -66 -133 -10 0 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2154434100001987 -19 0 -46 59 0 0 28 8 10 -130 -28 20 MANDALGOVI 45.77 106.28 1393 1228S 10FLxxno-9x-9WARM GRASS/SHRUBC
2222067400001968 60 62 13 -27 17 -14 -28 -5 -17 -130 -177 -137 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A
4257275500501925 30 -38 -8 23 13 3 -13 13 10 -128 -5 0 FOSSTON 47.57 -95.73 399 398R -9FLxxno-9x-9WARM MIXED B
4037104300051996 -62 1 44 58 59 -20 21 -18 -40 -127 13 62 NORMAN WELLS, 65.28 -126.80 74 94R -9HIxxno-9A-9MAIN TAIGA B
4257026400201924 -1 -66 0 -93 2 0 0 0 -14 -127 23 16 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A
2222067400061998 50 -63 0 -62 -25 2 0 7 -66 -126 -35 6 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A
4257453000501913 -19 -21 31 -5 -19 44 44 3 -47 -126 9 -63 EADS 2S 38.48 -102.78 1283 1275R -9FLxxno-9x-9COOL GRASS/SHRUBB
7008905500032002 22 9 51 61 -84 -44 10 -7 -9 -124 -30 -15 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
2154427200032000 -2 -60 31 5 -6 12 -14 -1 -9 -120 -68 0 ULIASTAI 47.75 96.85 1759 2525S 15MVxxno-9x-9TUNDRA A
4257223300301885 0 0 0 0 -21 6 -32 45 -23 -119 0 0 AMITE 30.70 -90.53 51 32R -9FLxxno-9x-9WARM FOR./FIELD A
4257258200301908 0 39 27 22 -27 -13 -1 -17 -56 -119 -11 -4 WELLS 41.12 -114.97 1722 1755R -9MVDEno-9x-9COOL IRRIGATED C
4257002600041988 14 33 -6 35 -3 -4 -1 -17 -1 -118 -46 -2 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C
2222321900041992 0 0 0 -148 -31 -78 0 4 3 -117 -94 82 HOSEDA-HARD 67.08 59.38 84 90R -9FLxxno-9x-9TUNDRA A
4257002600011988 15 31 -6 35 -2 -3 0 -17 1 -117 -43 -2 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C
4257002600031988 15 31 -6 35 -2 -3 0 -17 1 -117 -43 -2 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C
4037196600011939 55 44 -35 -17 -4 17 6 -14 -50 -116 12 102 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A
4257002600001988 14 31 -6 34 -2 -3 0 -17 0 -116 -46 -3 BARROW/W. POS 71.30 -156.78 4 0R -9FLMACO 1A-9TUNDRA C
4257275000101925 30 -34 -4 25 8 -6 -12 6 9 -116 -20 36 PINE RIVER DAM 46.67 -94.12 381 375R -9FLxxLA-9x-9COOL FIELD/WOODSB
2154420300012000 -70 -20 68 -1 -13 38 -14 11 19 -115 -60 0 RINCHINLHUMBE 51.12 99.67 1583 1720R -9MVxxno-9x-9SOUTH. TAIGA C
7008903400101962 -1 -12 -26 66 -129 48 68 19 1 -115 -48 0 BELGRANO -77.90 -34.50 55 0R -9FLICCO 1x-9ANTARCTICA A
4037196600001939 56 44 -36 -16 -4 16 7 -14 -51 -114 12 103 DAWSON,Y.T. 64.05 -139.13 370 926R -9MVxxno-9A-9WOODED TUNDRA A
4257264400101925 36 7 36 41 23 10 8 6 47 -114 -6 41 ALBERT LEA 3SE 43.62 -93.42 374 383S 18FLxxLA-9x-9COOL CROPS B
2222004600001963 2 -36 -88 -8 0 2 0 10 -10 -113 -130 -25 GMO IM.E.T. 80.62 58.05 20 0R -9HIxxCO 1x-9WATER A
4257265300401925 34 9 55 54 35 30 14 39 47 -113 -15 48 GANN VALLEY 4NW 44.07 -99.07 524 515R -9FLxxno-9x-9COOL CROPS A
4257265400401925 14 -6 44 43 27 10 0 22 36 -113 -4 45 WATERTOWN FAA AP 44.92 -97.15 532 529S 18FLxxLA-9A 2COOL CROPS C
2222333000021998 21 0 -3 -98 -34 0 70 21 -56 -112 -88 26 SALEHARD 66.53 66.67 16 30S 22FLxxno-9x-9BOGS, BOG WOODS B
4037149600101974 -23 -6 -28 -31 -45 -20 -18 -29 -60 -112 13 6 CONTWOYTO LAKE,NW 65.48 -110.37 451 439R -9FLxxLA-9x-9TUNDRA A
[chiefio@Hummer data]$ tail -30 Oct.Station.Rank
2222027400001967 -30 -20 72 17 17 0 1 -6 6 111 73 -57 OSTROV UEDINE 77.50 82.20 23 0R -9FLxxCO 1x-9WATER A
2222347200011978 40 81 21 -34 -14 -24 27 0 -3 111 -10 1 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A
2222347200041978 40 81 21 -34 -14 -24 27 1 -3 111 -10 1 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A
4257024900101957 156 19 50 18 17 47 -7 12 3 111 144 43 FAREWELL FAA AP 62.52 -153.89 457 417R -9FLxxno-9A-9TUNDRA A
6140283600001961 78 62 -4 -39 -49 16 -19 -9 -3 111 63 -38 SODANKYLA 67.37 26.65 179 191R -9FLFOno-9A-9MAIN TAIGA A
6140283600011961 78 64 -1 -40 -50 15 -20 -10 -3 111 64 -39 SODANKYLA 67.37 26.65 179 191R -9FLFOno-9A-9MAIN TAIGA A
2222347200031978 39 81 20 -34 -16 -22 33 5 -2 112 -8 3 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A
4037186300101920 0 61 31 -64 -5 -16 7 15 8 113 78 -17 YELLOW GRASS,SA 49.82 -104.18 580 575R -9FLxxno-9x-9COOL GRASS/SHRUBA
4257256600501926 11 -8 -13 -19 12 0 7 29 -10 113 -3 2 HARRISON 42.68 -103.88 1478 1468R -9HIxxno-9x-9COOL IRRIGATED A
2222307400001967 5 105 85 83 -4 21 53 9 -14 115 97 33 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C
2222307400031967 5 105 85 83 -4 41 53 9 -14 115 97 33 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C
2222347200001890 -55 -92 12 3 4 -16 -11 3 -33 115 -110 -28 TURUHANSK 65.78 87.93 38 64R -9FLxxno-9A-9NORTH. TAIGA A
2154434100001989 -27 14 61 0 33 0 0 -15 13 116 0 0 MANDALGOVI 45.77 106.28 1393 1228S 10FLxxno-9x-9WARM GRASS/SHRUBC
2154434100021989 -27 0 61 0 33 0 0 -15 13 116 0 0 MANDALGOVI 45.77 106.28 1393 1228S 10FLxxno-9x-9WARM GRASS/SHRUBC
2084085600022001 0 0 0 0 0 0 0 0 0 117 4 0 ZAHEDAN 29.47 60.88 1370 1580U 93HIxxno-9x-9WARM FIELD WOODSC
2222089100001967 39 89 85 47 5 24 73 -32 9 118 109 48 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100011967 41 87 80 45 4 27 42 -4 11 118 110 -12 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100021967 39 89 85 47 5 24 46 -7 9 118 130 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100031967 40 89 84 70 5 24 47 -6 9 118 109 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100041967 40 89 84 70 5 24 47 -6 9 118 109 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
2222089100051967 39 89 84 47 5 24 47 -6 9 118 109 -8 HATANGA 71.98 102.47 33 30R -9FLxxno-9A-9WOODED TUNDRA A
4037191100201973 26 30 -5 11 54 75 39 49 55 118 69 43 GLADMAN POINT A,NW 68.67 -97.80 25 9R -9FLxxCO 1A-9TUNDRA A
4037191100001973 51 27 8 -17 51 80 58 49 48 122 54 42 SHEPHERD BAY, 68.82 -93.43 42 48R -9FLxxCO 8A-9TUNDRA A
4257026400201925 -90 -22 -41 85 5 -1 -28 -27 30 125 -24 102 MCKINLEY PARK 63.72 -148.97 631 870R -9MVxxno-9A-9WOODED TUNDRA A
2154423900001989 -1 9 58 0 19 0 9 0 0 129 -7 0 BULGAN 48.80 103.55 1208 1484S 15HIxxno-9x-9COOL GRASS/SHRUBA
2154423900021989 -1 9 58 0 19 0 9 0 0 129 0 0 BULGAN 48.80 103.55 1208 1484S 15HIxxno-9x-9COOL GRASS/SHRUBA
2222307400001978 24 59 29 -54 -24 -31 31 6 -9 130 -28 -40 DUDINKA 69.40 86.17 19 50S 20FLxxno-9x-9WOODED TUNDRA C
2222314600021978 12 68 46 -103 0 -42 -25 -30 -7 131 -53 -42 MYS KAMENNYJ 68.47 73.58 8 8R -9FLxxCO 1A-9WATER B
2222314600031978 12 68 46 -103 0 -42 -25 -30 -7 131 -53 -42 MYS KAMENNYJ 68.47 73.58 8 8R -9FLxxCO 1A-9WATER B
2222067400001967 -27 48 82 55 8 11 8 7 3 173 94 -29 OSTROV DIKSON 73.50 80.40 47 0R -9HIxxCO 1A-9WATER A
[chiefio@Hummer data]$
July Station Rank, both “heads” and “tails”
Not surprising, July is dominated by Antarctica. But there are a few from other parts of the globe as well.
[chiefio@Hummer data]$ head -20 July.Station.Rank 7008960600061997 0 14 -11 12 11 36 -161 -38 26 -4 30 -25 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A 7008906600041994 0 0 11 37 17 -64 -145 -35 0 -70 0 0 BASE SAN MART -68.13 -67.13 4 233R -9MVICCO 1x-9WATER A 7008905500001990 5 29 15 13 -97 -24 -135 25 22 -56 -13 28 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 7018896800051994 0 0 -3 -9 -6 -50 -129 -29 43 -44 0 0 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A 7008906300001987 -9 -20 -21 -16 19 -49 -124 -17 -25 14 19 9 FARADAY -65.25 -64.27 11 0R -9HIICCO 1x-9WATER A 7008905500032007 -20 -30 -36 -94 -77 -90 -123 32 29 -75 -39 2 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 7008960600061993 7 -48 7 4 0 -61 -122 -35 -7 -43 -12 -24 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A 4257227400201889 -30 -87 0 -2 -45 -106 -121 -2 -49 6 -51 -63 TUCSON U OF AZ 32.23 -110.95 742 749U 667HIxxno-9x-9WARM GRASS/SHRUBC 7008905500031994 0 0 22 -75 31 -31 -120 0 -41 0 0 -28 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 3018896300021990 -7 13 -25 0 -92 14 -119 -47 0 -37 0 0 BASE ESPERANZ -63.40 -56.98 -999 45R -9HIICCO 1x-9WATER A 3018896300011990 -7 13 -27 -1 -92 15 -118 -47 18 -37 -11 -11 BASE ESPERANZ -63.40 -56.98 -999 45R -9HIICCO 1x-9WATER A 3018896300052007 -10 -21 -33 -77 -70 -62 -112 34 11 3 -25 8 BASE ESPERANZ -63.40 -56.98 -999 45R -9HIICCO 1x-9WATER A 7008900900062003 -12 1 -43 -43 -32 -24 -108 18 -51 -17 9 60 AMUNDSEN-SCOT -90.00 0.00 -999 2770R -9FLICno-9x-9WATER A 3018788000001982 0 0 0 21 24 -18 -107 -26 -8 -25 2 44 GOBERNADOR GR -48.78 -70.17 357 408R -9HIDEno-9A-9COOL DESERT A 7008906200001987 7 -7 -10 -22 50 -24 -106 7 -53 -7 14 1 ROTHERA POINT -67.57 -68.13 16 12R -9HIICCO 1x-9WATER A 7008960600001983 11 0 60 18 32 66 -106 51 106 -1 -10 -21 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A 7008966400041985 0 0 48 11 -30 0 -106 -8 0 -6 -15 -29 MCMURDO -77.85 166.67 24 0R -9FLICCO 1x-9WATER A 4257246900101911 0 0 0 0 -22 0 -103 -53 -25 -23 -16 33 DILLON 1E 39.63 -106.03 2763 2937R -9MVxxLA-9x-9COOL CONIFER C 7018896800031968 0 9 -5 -15 16 6 -102 20 17 43 0 7 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A 1016056000001909 -50 -40 -40 -39 -101 -76 -99 -29 -71 14 -43 -27 AIN SEFRA 32.77 -0.60 1058 1811R -9MVDEno-9x-9WARM GRASS/SHRUBA [chiefio@Hummer data]$ tail -20 July.Station.Rank 7008900100031973 20 -1 28 18 20 -3 114 -107 11 26 13 -17 S.A.N.A.E. ST -70.30 -2.35 62 0R -9FLICCO 1x-9ANTARCTICA A 7008906600001988 -7 2 5 19 8 43 115 -25 99 16 1 11 BASE SAN MART -68.13 -67.13 4 233R -9MVICCO 1x-9WATER A 7008905500011989 0 -10 0 -64 0 74 122 -7 -15 28 -21 0 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 7008905500021989 -28 -10 0 7 42 86 122 46 18 0 0 0 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 7008905500031989 0 -10 0 -64 0 74 122 -7 -15 28 -21 0 CMS"VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A 3048905700001989 0 8 20 -35 41 48 125 32 40 17 12 -3 BASE ARTURO P -62.50 -59.68 5 4R -9HIICCO 1x-9WATER A 3048905700011989 0 8 20 -35 41 48 125 32 40 15 10 -3 BASE ARTURO P -62.50 -59.68 5 4R -9HIICCO 1x-9WATER A 3048905700021989 0 8 20 -35 0 48 125 32 40 15 10 -3 BASE ARTURO P -62.50 -59.68 5 4R -9HIICCO 1x-9WATER A 7018896800021940 -2 1 9 -1 65 14 125 101 28 -37 12 10 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A 7008906200001988 -7 -3 7 16 6 38 127 -9 101 19 4 9 ROTHERA POINT -67.57 -68.13 16 12R -9HIICCO 1x-9WATER A 7008905900101989 0 7 12 -8 36 62 128 62 36 0 -3 -1 BERNADO O'HIGGINS -63.32 -57.90 -999 0R -9HIICCO 1x-9WATER A 7008905000021989 0 -2 22 -33 8 50 129 67 7 24 0 -1 BELLINGSHAUSE -62.20 -58.93 16 76R -9HIICCO 1x-9ANTARCTICA A 7008906200001962 0 0 0 0 125 -11 129 43 31 12 21 -6 ROTHERA POINT -67.57 -68.13 16 12R -9HIICCO 1x-9WATER A 7008900100001990 -23 26 -27 3 26 -12 137 40 6 88 43 13 S.A.N.A.E. ST -70.30 -2.35 62 0R -9FLICCO 1x-9ANTARCTICA A 7008900100021990 -23 26 -17 3 26 -12 137 40 6 88 43 13 S.A.N.A.E. ST -70.30 -2.35 62 0R -9FLICCO 1x-9ANTARCTICA A 1478904200001989 -6 -8 0 -36 29 55 139 37 35 30 21 -1 SIGNY ISLAND -60.72 -45.60 6 0R -9HIICCO 1x-9ANTARCTICA A 7008960600061995 -25 0 0 -2 -11 9 142 30 -3 -18 -4 35 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A 7008906300001988 -2 9 10 3 6 51 146 23 95 22 5 -1 FARADAY -65.25 -64.27 11 0R -9HIICCO 1x-9WATER A 7018896800001931 -15 -4 -7 26 21 91 148 43 -43 -24 -10 -4 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A 7018896800011931 -15 -5 -7 26 21 91 148 42 -43 -23 -10 -4 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A [chiefio@Hummer data]$
And Ragged Right:
[chiefio@Hummer data]$ head -20 July.Station.Rank
7008960600061997 0 14 -11 12 11 36 -161 -38 26 -4 30 -25 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A
7008906600041994 0 0 11 37 17 -64 -145 -35 0 -70 0 0 BASE SAN MART -68.13 -67.13 4 233R -9MVICCO 1x-9WATER A
7008905500001990 5 29 15 13 -97 -24 -135 25 22 -56 -13 28 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
7018896800051994 0 0 -3 -9 -6 -50 -129 -29 43 -44 0 0 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A
7008906300001987 -9 -20 -21 -16 19 -49 -124 -17 -25 14 19 9 FARADAY -65.25 -64.27 11 0R -9HIICCO 1x-9WATER A
7008905500032007 -20 -30 -36 -94 -77 -90 -123 32 29 -75 -39 2 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
7008960600061993 7 -48 7 4 0 -61 -122 -35 -7 -43 -12 -24 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A
4257227400201889 -30 -87 0 -2 -45 -106 -121 -2 -49 6 -51 -63 TUCSON U OF AZ 32.23 -110.95 742 749U 667HIxxno-9x-9WARM GRASS/SHRUBC
7008905500031994 0 0 22 -75 31 -31 -120 0 -41 0 0 -28 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
3018896300021990 -7 13 -25 0 -92 14 -119 -47 0 -37 0 0 BASE ESPERANZ -63.40 -56.98 -999 45R -9HIICCO 1x-9WATER A
3018896300011990 -7 13 -27 -1 -92 15 -118 -47 18 -37 -11 -11 BASE ESPERANZ -63.40 -56.98 -999 45R -9HIICCO 1x-9WATER A
3018896300052007 -10 -21 -33 -77 -70 -62 -112 34 11 3 -25 8 BASE ESPERANZ -63.40 -56.98 -999 45R -9HIICCO 1x-9WATER A
7008900900062003 -12 1 -43 -43 -32 -24 -108 18 -51 -17 9 60 AMUNDSEN-SCOT -90.00 0.00 -999 2770R -9FLICno-9x-9WATER A
3018788000001982 0 0 0 21 24 -18 -107 -26 -8 -25 2 44 GOBERNADOR GR -48.78 -70.17 357 408R -9HIDEno-9A-9COOL DESERT A
7008906200001987 7 -7 -10 -22 50 -24 -106 7 -53 -7 14 1 ROTHERA POINT -67.57 -68.13 16 12R -9HIICCO 1x-9WATER A
7008960600001983 11 0 60 18 32 66 -106 51 106 -1 -10 -21 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A
7008966400041985 0 0 48 11 -30 0 -106 -8 0 -6 -15 -29 MCMURDO -77.85 166.67 24 0R -9FLICCO 1x-9WATER A
4257246900101911 0 0 0 0 -22 0 -103 -53 -25 -23 -16 33 DILLON 1E 39.63 -106.03 2763 2937R -9MVxxLA-9x-9COOL CONIFER C
7018896800031968 0 9 -5 -15 16 6 -102 20 17 43 0 7 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A
1016056000001909 -50 -40 -40 -39 -101 -76 -99 -29 -71 14 -43 -27 AIN SEFRA 32.77 -0.60 1058 1811R -9MVDEno-9x-9WARM GRASS/SHRUBA
[chiefio@Hummer data]$ tail -20 July.Station.Rank
7008900100031973 20 -1 28 18 20 -3 114 -107 11 26 13 -17 S.A.N.A.E. ST -70.30 -2.35 62 0R -9FLICCO 1x-9ANTARCTICA A
7008906600001988 -7 2 5 19 8 43 115 -25 99 16 1 11 BASE SAN MART -68.13 -67.13 4 233R -9MVICCO 1x-9WATER A
7008905500011989 0 -10 0 -64 0 74 122 -7 -15 28 -21 0 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
7008905500021989 -28 -10 0 7 42 86 122 46 18 0 0 0 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
7008905500031989 0 -10 0 -64 0 74 122 -7 -15 28 -21 0 CMS”VICE.DO.M -64.23 -56.72 -999 0R -9HIICCO 1x-9WATER A
3048905700001989 0 8 20 -35 41 48 125 32 40 17 12 -3 BASE ARTURO P -62.50 -59.68 5 4R -9HIICCO 1x-9WATER A
3048905700011989 0 8 20 -35 41 48 125 32 40 15 10 -3 BASE ARTURO P -62.50 -59.68 5 4R -9HIICCO 1x-9WATER A
3048905700021989 0 8 20 -35 0 48 125 32 40 15 10 -3 BASE ARTURO P -62.50 -59.68 5 4R -9HIICCO 1x-9WATER A
7018896800021940 -2 1 9 -1 65 14 125 101 28 -37 12 10 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A
7008906200001988 -7 -3 7 16 6 38 127 -9 101 19 4 9 ROTHERA POINT -67.57 -68.13 16 12R -9HIICCO 1x-9WATER A
7008905900101989 0 7 12 -8 36 62 128 62 36 0 -3 -1 BERNADO O’HIGGINS -63.32 -57.90 -999 0R -9HIICCO 1x-9WATER A
7008905000021989 0 -2 22 -33 8 50 129 67 7 24 0 -1 BELLINGSHAUSE -62.20 -58.93 16 76R -9HIICCO 1x-9ANTARCTICA A
7008906200001962 0 0 0 0 125 -11 129 43 31 12 21 -6 ROTHERA POINT -67.57 -68.13 16 12R -9HIICCO 1x-9WATER A
7008900100001990 -23 26 -27 3 26 -12 137 40 6 88 43 13 S.A.N.A.E. ST -70.30 -2.35 62 0R -9FLICCO 1x-9ANTARCTICA A
7008900100021990 -23 26 -17 3 26 -12 137 40 6 88 43 13 S.A.N.A.E. ST -70.30 -2.35 62 0R -9FLICCO 1x-9ANTARCTICA A
1478904200001989 -6 -8 0 -36 29 55 139 37 35 30 21 -1 SIGNY ISLAND -60.72 -45.60 6 0R -9HIICCO 1x-9ANTARCTICA A
7008960600061995 -25 0 0 -2 -11 9 142 30 -3 -18 -4 35 VOSTOK -78.45 106.87 3420 3468R -9HIICno-9x-9ANTARCTICA A
7008906300001988 -2 9 10 3 6 51 146 23 95 22 5 -1 FARADAY -65.25 -64.27 11 0R -9HIICCO 1x-9WATER A
7018896800001931 -15 -4 -7 26 21 91 148 43 -43 -24 -10 -4 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A
7018896800011931 -15 -5 -7 26 21 91 148 42 -43 -23 -10 -4 BASE ORCADAS -60.75 -44.72 -999 0R -9HIICCO 1x-9WATER A
[chiefio@Hummer data]$
I did this listing as “head -30” and mostly you just get more years for the same stations.
A “mod flag compressed dT/dt” Teaser
I’ve gotten some code running to combine Mod Flags for a location via a simple average, then do a dT/dt run on that data. I’ve made a set of temperature data using that code and with the “cut off” or “start of time” being 1800 just to see what it looks like. I’ve not fully “vetted” the code and I’ve not “done it three times” to make sure the manual steps were not ‘screwed up’ in some way; so this must be thought of as an “experimental teaser” at present. I’ll be comfortable with it after a few more runs and some more vigorous QA is done.
But this is the kind of report that sent me off to look at those things shown above (though the above data comes from a data set that ‘begins time’ in 1880 as GIStemp does).
First up, of course, Canada. This is a report of the running total of “Delta T” over the years along with the specific dT in that year, the count of active thermometers in that year, then the average of the dT for those thermometers in each month compared to the prior month. Each new thermometer starts it’s life at the time it enters the series (so there is a ‘start of time’ bias for each thermometer. If it begins life in a frozen year, the “dT” to following years will show warming) Notice that this series starts in a cold time, then rapidly warms by several degrees. It was this exotic “10 C of Warming in 1931” (though in a non-mod-flag compressed version) that got me looking at the Canadian stations in particular and sent me off to do the monthly ranking sorts.
Produced from input file: ./DTemps/Temps.M403 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1811 0.000 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1812 0.342 0.34 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 1.2 3.0 -0.4 -0.8 1813 1.925 1.58 1 4.3 0.0 -0.2 8.2 5.3 1.7 1.9 -3.6 1.4 0.0 0.0 0.0 1814 1.925 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1815 1.242 -0.68 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.4 -6.5 -3.1 1816 1.892 0.65 2 4.4 -4.2 1.9 3.9 3.9 1.9 0.0 0.0 0.0 -3.0 -1.0 0.0 1817 3.192 1.30 1 -8.4 8.2 6.0 -1.7 -4.9 1.2 -0.6 6.6 -4.1 5.6 10.0 -2.3 1818 4.967 1.77 1 2.6 -6.1 0.9 6.4 4.3 6.3 0.0 -0.6 0.0 0.6 4.1 2.8 1819 4.300 -0.67 1 0.2 5.0-13.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1821 5.467 1.17 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.8 0.2 1822 5.592 0.12 1 0.9 9.2 11.3 0.0 0.0 0.0 0.0 0.0 -2.0 -3.7 -8.6 -5.6 1823 4.750 -0.84 1 3.2 -5.1 -7.9 -3.3 -5.3 0.0 0.0 0.0 0.0 0.5 2.3 5.5 1824 7.358 2.61 1 -0.8 -2.0 2.7 6.3 5.4 8.3 2.6 0.0 7.3 0.5 -0.9 1.9 1825 7.933 0.57 1 -3.1 6.5 4.2 3.6 0.0 0.0 0.0 0.0 0.0 -1.9 -2.8 0.4 1826 8.392 0.46 1 1.2 -3.5 -3.0 -3.3 0.0 0.0 0.0 0.0 0.0 1.7 7.9 4.5 1827 8.383 -0.01 1 8.0 2.1 -2.3 3.3 3.8 -4.9 -4.3 0.2 -3.8 5.8 -2.2 -5.8 1828 7.758 -0.62 1 -9.0 0.7 6.2 -1.8 -3.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1829 7.775 0.02 1 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.0 1.8 -1.9 -2.3 1.4 1830 9.908 2.13 1 0.2 5.7 -2.1 2.2 -2.3 -1.2 2.8 -0.2 -0.5 2.9 11.6 6.5 1831 9.092 -0.82 1 8.4 -0.6 1.7 -4.2 0.8 -0.4 -3.0 2.4 1.4 -1.6 -4.8 -9.9 1832 8.067 -1.02 1 -2.1 -9.9 -1.2 -0.6 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1834 8.067 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1835 8.892 0.82 1 4.0 4.9 1.0 1.7 -0.1 -0.8 0.2 1.4 -0.2 -0.8 -0.9 -0.5 1836 8.300 -0.59 1 0.9 -0.6 0.6 -1.1 -1.3 -1.7 -1.5 -1.9 -3.5 -1.4 2.5 1.9 1837 8.342 0.04 1 0.4 -2.3 2.2 3.1 1.3 0.3 -1.5 -0.5 1.1 -1.8 0.5 -2.3 1838 6.317 -2.02 3 -4.3 -0.3 2.3 -7.4 -3.7 -2.1 0.3 -2.9 1.0 0.7 -7.2 -0.7 1839 7.575 1.26 3 0.6 3.9 -2.9 7.4 0.6 -0.1 1.6 1.9 -1.1 -1.1 1.9 2.4 1840 7.858 0.28 4 1.2 -1.6 1.1 -1.9 0.2 0.8 1.7 0.1 0.5 0.0 1.1 0.2 1841 7.633 -0.23 4 0.5 -1.2 -1.0 -4.0 0.5 0.2 -1.0 0.6 0.0 -0.3 1.3 1.7 1842 7.858 0.22 4 0.2 0.6 2.6 4.7 -0.5 -2.7 -1.3 0.9 0.3 1.1 -2.4 -0.8 1843 6.883 -0.98 4 2.2 -2.9 -4.9 -1.8 -1.6 0.3 0.0 -1.3 -0.3 -2.0 0.1 0.5 1844 7.142 0.26 4 -5.2 5.2 2.0 2.2 1.2 1.0 -1.0 -1.1 0.0 1.4 -1.5 -1.1 1845 7.242 0.10 3 5.0 -0.2 1.5 -2.2 -1.3 0.4 0.0 1.4 -0.8 -1.2 1.0 -2.4 1846 8.258 1.02 3 1.9 -3.0 1.6 -1.5 0.9 2.8 0.8 0.9 1.9 1.1 2.8 2.0 1847 7.033 -1.23 2 -1.8 0.7 -2.5 -4.2 -0.9 -1.4 -0.2 -1.0 -2.8 -0.2 -0.8 0.4 1848 6.992 -0.04 2 -3.9 4.9 -2.3 4.3 1.0 1.1 -0.6 -0.6 -2.0 0.3 -1.2 -1.5 1849 6.608 -0.38 2 -1.4 -6.5 2.3 -5.7 -1.1 0.0 1.6 -0.1 1.4 1.2 5.3 -1.6 1850 7.583 0.97 2 3.2 6.2 -0.7 1.9 -0.4 3.1 0.6 1.4 0.2 -1.5 -2.6 0.3 1851 7.250 -0.33 2 -1.6 -2.6 1.3 2.4 1.4 -3.3 -1.9 -2.4 1.5 1.5 -2.6 2.3 1852 7.425 0.17 4 -0.3 0.4 -2.2 0.5 0.3 0.5 0.3 1.2 0.3 0.0 0.4 0.7 1853 7.433 0.01 4 0.4 0.0 0.5 0.4 -0.2 0.5 -0.1 -0.8 -0.2 0.0 0.6 -1.0 1854 7.433 0.00 3 -0.4 -1.0 -0.3 -0.9 0.0 -1.1 2.9 0.4 0.1 1.0 -0.7 0.0 1855 7.392 -0.04 5 1.3 -0.6 -0.5 0.6 0.2 0.0 -0.1 -0.5 -0.7 -0.1 0.2 -0.3 1856 7.367 -0.03 5 -1.7 -0.4 -0.5 1.7 1.0 1.1 -0.6 -0.2 0.6 -0.4 -0.1 -0.8 1857 7.758 0.39 5 -0.1 4.5 1.7 -1.9 -0.5 -1.7 -0.3 0.5 -0.5 0.0 0.3 2.7 1858 7.825 0.07 6 2.6 -4.4 1.5 1.3 -0.3 -0.3 -0.3 0.5 1.2 1.1 -1.2 -0.9 1859 7.692 -0.13 5 -1.9 1.6 2.2 -0.8 2.0 -1.5 -0.2 0.8 -1.6 -2.3 0.7 -0.6 1860 8.383 0.69 4 1.1 -0.9 0.1 -0.3 0.2 1.5 -0.1 -0.3 1.0 1.9 2.7 1.4 1861 8.383 -0.00 4 -1.0 1.9 -2.2 0.6 -2.0 0.2 1.5 -0.7 0.5 0.7 -0.2 0.7 1862 8.192 -0.19 2 -0.3 -2.5 0.5 -0.7 1.0 -0.2 0.5 0.2 0.3 -0.1 -0.5 -0.5 1863 8.467 0.27 1 3.7 0.5 -1.6 1.0 1.8 0.2 0.0 -0.2 -2.6 -0.8 2.0 -0.7 1864 8.142 -0.33 2 -0.5 -0.1 0.3 -0.2 0.1 0.1 0.3 0.6 -0.8 -1.8 -1.5 -0.4 1865 8.067 -0.08 4 -0.3 0.7 0.4 1.0 0.4 0.0 -1.7 -0.1 0.5 -0.7 -0.5 -0.6 1866 7.817 -0.25 11 -0.3 -0.1 -1.0 0.0 -0.8 -0.5 0.6 -1.0 -0.6 0.7 0.0 0.0 1867 7.942 0.12 11 0.0 1.8 0.0 -1.7 -0.5 1.3 -1.3 2.8 1.3 0.0 -0.3 -1.9 1868 7.567 -0.38 12 0.0 -6.6 2.5 -1.3 2.9 -1.1 3.8 0.2 -0.8 -3.1 -1.2 0.2 1869 7.842 0.27 12 5.2 4.7 -4.3 1.2 -0.4 -1.7 -4.5 -1.5 2.0 0.0 -1.1 3.7 1870 8.808 0.97 14 -1.6 -1.4 1.1 2.0 2.0 3.2 1.6 1.3 0.0 2.7 1.7 -1.0 1871 8.108 -0.70 17 -1.2 0.7 3.5 -0.3 -0.6 -1.7 -1.1 -0.1 -2.3 -0.1 -2.6 -2.6 1872 8.025 -0.08 28 0.1 -0.2 -3.6 -0.6 -0.2 0.6 1.0 0.5 1.7 -0.4 0.9 -0.8 1873 7.925 -0.10 31 -0.9 -0.7 2.3 -0.3 0.0 -0.1 -0.4 -0.6 -1.1 -0.1 -2.1 2.8 1874 8.058 0.13 35 2.0 0.0 0.1 -1.8 0.1 -0.6 -0.2 -0.2 0.9 0.4 1.4 -0.5 1875 6.975 -1.08 35 -3.9 -3.0 -1.8 0.7 -0.4 0.4 -0.4 0.4 -1.5 -1.6 -1.9 0.0 1876 7.867 0.89 41 3.5 2.8 0.9 0.8 0.0 1.1 0.7 0.5 0.0 -0.2 3.3 -2.7 1877 8.900 1.03 52 -2.8 3.8 0.1 1.5 0.7 -1.0 0.2 0.2 1.6 1.2 1.2 5.7 1878 9.325 0.43 59 2.6 -1.0 3.6 1.8 0.0 -0.1 0.1 -0.1 0.0 1.0 0.0 -2.8 1879 7.842 -1.48 62 -1.9 -4.3 -3.5 -3.0 0.7 -0.5 -0.9 -1.2 -1.3 1.3 -1.4 -1.8 1880 8.225 0.38 57 4.1 2.9 -1.6 -0.1 0.6 1.1 0.3 0.2 1.0 -2.5 -1.6 0.2 1881 8.567 0.34 63 -4.8 -1.0 3.4 -0.1 0.0 -1.7 0.0 1.1 1.6 -0.5 1.5 4.6 1882 8.158 -0.41 67 1.4 1.7 -1.7 -0.7 -2.8 0.6 -0.5 -0.2 -1.4 1.5 0.1 -2.9 1883 6.983 -1.17 68 -2.5 -3.6 -3.2 0.3 0.1 0.8 -0.3 -1.1 -1.5 -2.4 0.2 -0.9 1884 7.533 0.55 71 -0.2 1.0 2.0 1.2 0.8 0.5 -0.8 0.6 1.5 0.9 -0.6 -0.3 1885 7.283 -0.25 79 0.9 -2.7 -3.0 -0.6 0.2 -1.4 1.7 -1.2 -1.1 0.0 1.9 2.3 1886 7.883 0.60 79 0.5 2.7 3.6 2.1 0.3 0.5 0.0 1.0 0.1 0.9 -1.3 -3.2 1887 7.500 -0.38 78 -1.4 -1.7 -1.1 -2.4 1.8 0.6 1.2 -0.7 -0.3 -1.9 -0.6 1.9 1888 7.383 -0.12 79 -0.7 1.2 -0.4 -0.6 -3.0 -0.1 -1.6 0.0 0.5 0.0 0.9 2.4 1889 8.883 1.50 82 6.5 -0.5 4.9 3.2 1.8 -0.4 0.4 0.5 1.0 0.0 0.9 -0.3 1890 7.933 -0.95 90 -3.3 1.0 -3.3 -1.7 -2.1 0.3 0.0 -0.4 -0.8 1.3 0.0 -2.4 1891 8.608 0.68 89 2.8 -0.3 0.1 1.6 0.8 -0.5 -1.2 0.4 1.7 -0.1 -1.1 3.9 1892 8.317 -0.29 93 -1.4 2.0 0.7 -1.2 -0.7 0.3 1.2 0.3 -0.7 0.2 -0.5 -3.7 1893 7.525 -0.79 101 -2.5 -3.2 -1.3 -1.8 0.7 0.7 -0.1 0.1 -1.1 0.1 0.0 -1.1 1894 8.633 1.11 104 1.7 1.1 3.0 2.5 0.5 0.1 0.8 -0.3 0.8 0.2 -0.3 3.2 1895 8.408 -0.23 114 -0.3 1.0 -2.4 1.1 0.9 -0.1 -1.2 -0.4 -0.4 -1.6 0.8 -0.1 1896 8.200 -0.21 115 0.0 1.6 -0.7 -1.1 0.0 -0.1 0.8 0.3 -0.5 0.4 -2.8 -0.4 1897 8.408 0.21 126 1.1 -0.8 0.0 0.3 -0.4 -1.1 0.3 0.0 1.5 1.2 0.9 -0.5 1898 9.000 0.59 130 0.5 1.1 2.8 0.0 0.4 1.1 -0.1 0.8 0.1 -0.9 1.2 0.1 1899 8.333 -0.67 134 -1.0 -3.7 -4.0 -0.2 -1.2 -0.6 -0.4 -0.8 -1.1 0.5 3.5 1.0 1900 9.108 0.78 136 2.4 1.6 1.5 1.8 1.0 0.8 0.0 0.7 0.7 1.7 -3.0 0.1 1901 9.017 -0.09 132 -2.0 0.2 1.6 -0.7 0.5 -0.7 0.7 0.1 -0.5 -0.6 0.4 -0.1 1902 8.875 -0.14 138 1.2 2.5 2.1 -0.5 -1.0 -1.7 -0.8 -0.9 0.1 -0.9 0.9 -2.7 1903 8.533 -0.34 141 -0.5 -1.4 -1.2 -0.5 -0.2 1.4 -0.4 -1.0 -0.7 0.4 -1.1 1.1 1904 7.808 -0.72 139 -2.1 -4.9 -3.0 -0.5 0.4 0.0 0.2 0.3 0.0 -0.5 2.3 -0.9 1905 8.575 0.77 142 -0.3 2.9 3.7 0.7 -0.8 -0.2 0.6 0.9 1.2 -1.3 -1.0 2.8 1906 9.050 0.47 140 4.1 2.3 -2.5 1.3 0.0 0.8 0.7 0.7 0.6 1.7 -0.5 -3.5 1907 7.675 -1.38 150 -6.4 -1.9 0.3 -4.5 -2.3 -0.5 -1.3 -1.9 -1.3 -0.7 0.8 3.2 1908 8.700 1.02 161 5.0 1.4 -0.8 1.9 2.7 0.3 1.0 0.4 1.4 0.3 0.3 -1.6 1909 8.125 -0.57 164 -3.3 -0.6 1.8 -1.7 -0.7 0.2 -0.7 0.7 -0.2 -0.3 -1.4 -0.7 1910 8.958 0.83 168 3.9 -1.3 3.7 4.2 0.1 0.0 0.5 -0.9 -1.1 0.8 -0.1 0.2 1911 8.325 -0.63 175 -4.5 1.3 -3.1 -2.3 1.3 0.3 -0.4 0.3 -0.3 -0.5 -1.8 2.1 1912 8.400 0.07 189 -0.4 1.0 -2.5 0.5 -0.2 -0.3 -0.5 -0.6 0.1 0.1 3.6 0.1 1913 8.667 0.27 208 2.1 -1.9 0.7 1.1 -1.1 0.0 0.1 1.0 0.4 -0.7 0.3 1.2 1914 8.542 -0.12 228 1.6 -1.4 1.7 -1.5 1.1 -0.5 1.0 -0.2 0.2 1.9 -1.1 -4.3 1915 9.292 0.75 251 0.2 5.0 0.7 3.3 -0.6 -0.6 -1.3 0.7 -0.3 -0.7 0.1 2.5 1916 7.933 -1.36 256 -4.0 -3.0 -3.1 -2.3 -0.6 0.2 1.7 -0.2 0.1 -1.3 -0.5 -3.3 1917 7.458 -0.47 258 1.3 -2.4 1.2 -1.9 -0.5 -0.4 -0.2 -0.3 0.1 -1.2 2.2 -3.6 1918 8.475 1.02 274 0.3 1.6 1.1 1.6 0.5 0.4 -0.9 0.0 -0.6 2.1 -0.5 6.6 1919 8.550 0.07 265 5.6 1.9 -1.8 0.2 1.2 2.2 0.9 0.5 1.0 -3.0 -4.0 -3.8 1920 8.642 0.09 271 -6.3 1.1 1.1 -2.9 -0.6 -1.5 -0.3 0.5 0.3 3.8 2.4 3.5 1921 9.233 0.59 276 4.0 0.6 0.4 3.0 1.0 1.5 1.3 -1.2 -0.4 -0.1 -2.1 -0.9 1922 8.667 -0.57 285 -1.5 -4.2 0.0 -0.4 0.5 -0.5 -1.6 0.8 0.6 -0.8 2.8 -2.5 1923 8.783 0.12 295 -0.4 0.4 -3.0 -1.2 -1.4 0.1 0.4 -1.3 -0.2 0.8 1.7 5.5 1924 8.408 -0.38 300 -0.5 3.5 4.3 0.1 -0.7 -1.7 -0.3 0.1 -0.6 0.6 -2.8 -6.5 1925 8.725 0.32 300 -0.5 -0.7 -0.7 2.2 0.7 1.0 0.0 0.9 -0.2 -4.6 0.2 5.5 1926 8.700 -0.03 312 4.3 1.2 -0.3 -1.7 0.3 -1.1 0.3 -0.8 -1.6 2.7 -1.0 -2.6 1927 8.267 -0.43 303 -2.6 -1.9 1.2 -0.1 -1.5 0.3 -0.2 0.2 2.2 1.2 -1.6 -2.4 1928 9.192 0.93 299 1.6 1.6 -1.1 -1.2 2.5 0.0 0.3 0.0 -0.8 -1.4 3.6 6.0 1929 8.300 -0.89 310 -4.3 -3.4 1.0 1.0 -1.9 0.2 -0.3 0.3 0.3 1.3 -0.8 -4.1 1930 9.275 0.98 318 -0.5 3.6 -0.9 1.7 0.7 1.1 0.5 0.9 0.4 -1.6 1.2 4.6 1931 10.342 1.07 321 6.6 2.7 0.5 0.5 0.6 -0.1 0.6 -0.5 0.5 2.2 -0.1 -0.7 1932 8.808 -1.53 321 -1.3 -5.0 -3.2 -1.0 0.4 0.2 -1.4 0.6 -0.1 -2.2 -2.7 -2.7 1933 8.333 -0.47 323 -0.6 -0.3 1.9 -0.5 -0.5 0.0 0.6 -0.1 -0.5 -0.5 0.0 -5.2 1934 9.058 0.73 334 0.4 0.5 0.0 1.4 1.1 -0.9 0.0 -1.5 -0.7 1.5 3.0 3.9 1935 8.358 -0.70 338 -5.0 2.5 -1.0 -2.3 -2.2 -0.1 1.1 0.5 0.2 -0.9 -3.4 2.2 1936 8.242 -0.12 338 1.3 -9.1 2.8 -0.2 2.1 0.6 0.0 0.4 0.2 0.0 1.3 -0.8 1937 9.000 0.76 344 -0.8 6.5 -1.0 2.1 0.0 0.7 -0.1 0.6 0.7 1.0 0.1 -0.7 1938 9.575 0.58 351 3.4 -1.1 1.2 -0.3 -0.8 0.0 -0.1 -0.4 1.3 1.5 0.0 2.2 1939 9.092 -0.48 353 1.1 -1.8 -3.4 -0.3 0.3 -1.9 -0.1 0.6 -1.8 -3.1 1.9 2.7 1940 9.233 0.14 352 -1.7 4.3 2.4 -0.2 0.3 0.4 -0.3 -0.5 1.8 1.8 -3.6 -3.0 1941 9.600 0.37 357 0.2 0.6 0.0 2.4 0.0 1.3 1.0 -0.9 -2.6 -0.5 2.6 0.3 1942 9.425 -0.17 371 1.9 -0.6 2.2 -0.6 -0.2 -0.7 -1.4 0.6 0.8 1.1 -1.9 -3.3 1943 8.883 -0.54 383 -4.8 0.8 -5.1 -1.3 -1.5 -0.8 0.4 -0.3 0.0 0.2 2.7 3.2 1944 9.692 0.81 393 6.6 -1.5 0.8 0.6 2.4 0.8 -0.3 0.7 0.8 -0.1 -0.5 -0.6 1945 8.892 -0.80 394 -3.3 1.0 4.6 -1.4 -2.6 -1.0 -0.1 0.1 -1.4 -1.1 -2.8 -1.6 1946 9.050 0.16 392 0.1 -2.3 0.7 1.7 0.9 0.4 -0.1 -0.7 0.9 -0.1 0.5 -0.1 1947 9.117 0.07 395 -0.4 0.9 -3.0 -2.1 -0.8 -0.1 1.0 0.6 -0.7 2.3 1.2 1.9 1948 8.850 -0.27 409 0.4 -2.4 -2.9 -0.5 1.4 0.9 -0.6 0.0 1.3 -1.4 1.8 -1.2 1949 9.150 0.30 407 -0.9 0.1 2.6 3.4 -0.1 0.1 0.0 0.3 -1.0 -0.6 -0.1 -0.2 1950 7.950 -1.20 422 -4.9 1.2 -2.0 -3.4 -0.5 -0.4 -0.6 -1.7 -0.1 -0.5 -2.9 1.4 1951 8.433 0.48 441 3.8 1.4 0.3 2.4 1.1 -0.5 0.3 0.2 -0.1 -0.6 0.0 -2.5 1952 9.617 1.18 441 -0.5 1.5 1.2 1.3 -0.4 0.5 0.4 0.6 0.9 0.8 2.8 5.1 1953 10.000 0.38 449 2.0 1.2 1.2 -1.6 -0.3 0.0 -0.4 0.4 -0.4 1.6 1.4 -0.5 1954 9.033 -0.97 450 -4.4 1.4 -1.5 -3.2 -1.0 0.0 -0.4 -0.9 -0.5 -0.9 -0.2 0.0 1955 8.625 -0.41 455 5.2 -4.3 -2.3 3.7 0.8 0.9 1.2 1.1 -0.2 0.2 -5.9 -5.3 1956 8.458 -0.17 458 -0.7 -0.5 0.7 -1.9 -1.3 -0.7 -1.3 -1.1 -0.7 -0.6 4.0 2.1 1957 8.925 0.47 476 -4.0 0.6 2.8 1.0 1.2 -0.2 0.4 -0.3 1.8 -0.3 0.1 2.5 1958 9.458 0.53 478 7.1 0.0 1.0 1.1 0.7 -0.4 0.2 1.0 -0.5 0.6 -1.1 -3.3 1959 8.817 -0.64 482 -5.0 -1.5 -1.4 -0.9 -1.1 0.5 0.6 -0.5 0.0 -1.8 -1.0 4.4 1960 9.317 0.50 488 1.6 3.6 -2.4 0.0 1.3 0.2 -0.3 0.5 0.3 1.8 1.5 -2.1 1961 9.208 -0.11 491 -0.4 -1.0 2.1 -0.5 -1.2 0.9 0.2 0.7 -0.3 -0.3 0.0 -1.5 1962 8.975 -0.23 500 -0.4 -3.6 0.1 0.4 0.0 -0.6 -1.4 -1.0 -0.1 1.1 1.1 1.6 1963 9.258 0.28 503 -0.3 2.5 -0.4 0.6 -0.2 0.0 1.3 0.0 0.5 1.5 -0.2 -1.9 1964 8.792 -0.47 508 2.7 2.5 -2.2 -0.8 0.8 -0.6 -0.1 -1.2 -1.3 -2.3 -1.3 -1.8 1965 8.400 -0.39 521 -3.1 -4.1 0.8 0.0 -0.4 0.1 -0.8 0.9 -0.8 -0.1 -1.1 3.9 1966 8.808 0.41 536 -1.4 2.1 2.8 -0.7 -0.5 0.3 0.8 0.0 2.3 -0.5 -0.1 -0.2 1967 8.775 -0.03 542 3.2 -2.3 -3.9 -1.0 -0.8 0.4 0.0 0.9 0.8 0.4 1.7 0.2 1968 9.042 0.27 547 -1.7 1.8 4.9 2.7 1.0 -1.0 -0.5 -2.0 -0.4 0.8 0.2 -2.6 1969 9.092 0.05 564 -1.8 1.1 -2.7 0.0 0.0 0.3 -0.2 1.7 -1.1 -1.9 1.0 4.2 1970 8.742 -0.35 576 1.1 -0.3 0.2 -0.9 -0.2 1.1 1.0 0.1 -0.3 1.2 -1.7 -5.5 1971 8.817 0.07 571 -0.8 0.0 -0.3 0.5 1.1 -0.5 -0.9 0.0 0.9 0.4 0.1 0.4 1972 7.683 -1.13 572 -0.2 -4.2 -0.6 -1.8 -0.1 -0.3 -0.5 -0.5 -2.0 -2.7 -0.1 -0.6 1973 9.392 1.71 590 4.1 2.9 3.8 1.6 -0.1 0.4 1.4 0.5 1.8 2.5 -2.0 3.6 1974 8.658 -0.73 596 -3.6 -0.1 -4.9 -0.4 -2.0 0.0 -0.6 -1.0 -1.0 -1.2 3.5 2.5 1975 8.842 0.18 593 2.5 -1.1 0.7 -0.9 2.4 0.1 1.6 -0.2 0.9 0.6 -0.6 -3.8 1976 9.233 0.39 584 0.3 2.5 0.6 3.1 -0.1 -0.1 -1.6 0.8 0.5 -1.0 -0.1 -0.2 1977 9.692 0.46 579 0.0 2.8 3.4 0.0 0.9 0.0 -0.1 -0.7 -0.9 1.5 -0.1 -1.3 1978 8.767 -0.92 575 -1.3 -3.2 -2.8 -1.9 -0.9 -0.2 0.2 0.2 -0.2 -0.6 -1.9 1.5 1979 9.042 0.27 571 0.0 -5.8 1.6 -0.3 -0.6 0.0 0.8 0.2 1.0 0.5 3.1 2.8 1980 9.217 0.17 561 0.8 5.9 -1.5 2.8 1.4 -0.3 -0.9 0.0 -1.3 -0.3 0.0 -4.5 1981 10.475 1.26 553 3.7 3.2 4.0 -1.9 -0.5 -0.2 0.6 1.4 1.2 -0.6 1.1 3.1 1982 8.308 -2.17 544 -8.2 -5.5 -4.8 -2.0 -0.7 0.2 -0.3 -2.4 0.0 0.7 -3.6 0.6 1983 9.508 1.20 535 6.8 3.5 2.0 2.2 -1.0 0.8 0.2 2.2 -0.2 0.0 2.7 -4.8 1984 9.442 -0.07 524 -1.3 2.9 -0.6 1.2 0.4 -0.4 0.1 0.1 -1.5 -0.9 -2.4 1.6 1985 8.792 -0.65 511 0.6 -5.4 0.8 -1.7 1.2 -0.9 -0.2 -1.8 0.7 0.2 -3.4 2.1 1986 9.442 0.65 506 2.0 0.5 0.6 0.5 0.2 0.3 -0.9 0.4 -0.4 0.5 1.7 2.4 1987 10.683 1.24 499 0.4 3.2 -0.2 1.9 0.0 1.4 1.1 -0.6 2.3 0.2 4.4 0.8 1988 9.908 -0.78 500 -3.3 -2.7 0.4 -0.9 0.6 0.0 0.1 1.3 -1.1 -0.1 -0.9 -2.7 1989 9.042 -0.87 496 0.6 -2.2 -4.3 -0.8 -0.6 -0.1 0.4 0.1 0.6 0.0 -2.0 -2.1 1990 9.392 0.35 277 1.9 1.5 4.1 0.3 -1.0 -0.3 0.1 0.2 0.1 -0.5 -0.4 -1.8 1991 9.742 0.35 44 -3.3 4.5 -1.4 0.3 1.1 0.3 0.0 0.4 -0.4 0.1 1.0 1.6 1992 9.075 -0.67 41 3.5 -2.1 0.4 -2.3 -1.7 -1.5 -1.8 -1.2 -0.9 0.2 -0.2 -0.4 1993 9.167 0.09 41 -1.3 -0.7 0.0 1.6 1.7 0.8 1.1 0.5 0.7 0.3 -0.5 -3.1 1994 9.250 0.08 37 -2.1 -1.6 0.2 -0.1 -0.1 0.9 1.2 0.1 0.9 1.6 0.0 0.0 1995 9.592 0.34 37 3.7 2.2 -0.6 0.4 -3.2 0.5 -0.3 0.0 -0.1 -0.5 0.0 2.0 1996 9.425 -0.17 39 -3.6 1.9 -1.0 -0.1 0.7 -0.7 -0.2 0.0 -0.1 -1.8 0.2 2.7 1997 9.783 0.36 39 1.5 -1.3 -0.8 -0.6 0.7 0.0 0.2 -0.1 0.4 0.5 1.7 2.1 1998 11.242 1.46 44 -0.3 2.7 2.4 2.8 2.9 0.8 0.7 1.1 1.1 1.6 1.7 0.0 1999 11.083 -0.16 48 0.9 0.2 2.0 -0.6 -1.3 -0.6 -1.1 -0.5 -0.2 -1.4 0.0 0.7 2000 10.383 -0.70 48 0.0 -0.6 0.3 -1.4 -1.2 -1.2 0.4 -0.3 -1.4 0.7 -0.4 -3.3 2001 10.967 0.58 48 2.6 -2.3 -1.3 0.3 1.6 0.6 0.0 0.8 1.5 0.2 0.0 3.0 2002 10.050 -0.92 39 -1.8 0.0 -3.8 -2.0 -2.2 0.1 -0.4 -0.5 -0.5 -0.6 -0.5 1.2 2003 10.642 0.59 36 1.6 -1.5 0.4 1.4 2.4 0.0 0.4 0.4 0.6 2.2 0.0 -0.8 2004 9.975 -0.67 36 -4.1 3.2 1.2 0.4 -2.0 -0.5 -0.1 -0.9 -1.3 -1.0 0.4 -3.3 2005 10.767 0.79 44 0.2 -0.1 1.0 1.5 1.8 0.5 0.0 0.2 0.5 0.6 0.6 2.7 2006 11.667 0.90 40 3.7 1.9 2.1 0.8 0.5 1.1 0.3 0.1 0.4 -0.1 -1.2 1.2 2007 10.342 -1.32 44 0.1 -2.1 -3.2 -1.5 -2.5 -0.9 0.2 -0.4 -1.3 -0.2 -0.1 -4.0 2008 10.400 0.06 47 -0.7 -1.1 0.0 -0.1 1.5 0.1 0.0 0.0 0.4 0.1 1.7 -1.2 2009 10.292 -0.11 35 -2.2 1.3 -1.2 -0.3 -1.7 0.0 -1.0 0.0 1.1 -1.8 0.9 3.6 2010 10.633 0.34 35 4.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 403 From input file data/EMS.inv11.M1800.dt
What I see in the report is warming out of the Little Ice Age then rolling with the PDO. YMMV… Then there is a ‘warm tail’ hockey stick right in phase with the “splice time” seen in the above examples.
For comparison, the Pacific Basin (that includes Australia, New Zealand, Indonesia, etc. but leaves out Japan and Hawaii… go figure) is rather dead flat and with a little net cooling, then with a valiant attempt at a bit of a hockey stick lift at the end with the ‘survivor bias’ issues in the thermometers that are kept vs tossed:
Produced from input file: ./DTemps/Temps.M5 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1825 0.000 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1839 -0.217 -0.22 1 0.0 0.0 0.0 -0.2 -0.7 -0.5 -1.2 -0.4 -0.1 0.1 -0.3 0.7 1840 0.017 0.23 1 0.9 0.4 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.0 1841 0.008 -0.01 2 -0.3 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1842 -0.750 -0.76 1 -2.3 -1.1 -1.1 -0.2 0.0 -0.7 -0.6 -0.6 0.1 -2.2 -0.3 -0.1 1843 -0.192 0.56 1 -0.2 0.1 0.1 0.3 0.5 1.7 1.3 0.3 -1.2 2.2 0.0 1.6 1844 -1.083 -0.89 1 -0.4 0.3 -1.6 -1.2 -0.2 -1.7 -0.3 -0.9 -0.2 -1.0 -2.1 -1.4 1845 -0.042 1.04 1 1.4 -1.3 1.6 2.4 -0.8 0.0 1.7 1.2 1.8 1.0 2.3 1.2 1846 -0.325 -0.28 1 -0.2 -0.9 -0.6 -0.7 0.3 0.8 -1.4 -1.2 -1.5 0.1 0.7 1.2 1847 -0.492 -0.17 1 -0.3 1.2 -0.4 -0.1 -1.0 -1.4 0.3 1.9 1.1 -0.7 -2.2 -0.4 1848 -1.283 -0.79 1 -1.3 -1.7 1.0 2.5 0.5 1.0 -1.2 -1.7 -2.5 -1.4 -1.0 -3.7 1849 -1.633 -0.35 1 -0.7 -1.1 -2.7 0.0 -0.9 -0.9 0.6 0.6 -0.8 1.0 -0.2 0.9 1850 -1.108 0.53 1 0.0 1.1 2.8 -2.2 1.7 1.5 -0.3 -0.8 0.0 0.8 0.8 0.9 1851 -0.792 0.32 1 1.0 1.5 -1.8 0.9 -0.5 0.2 1.5 0.8 2.1 -1.2 -1.0 0.3 1852 -1.175 -0.38 1 -0.9 0.4 1.5 -1.0 -0.7 -1.4 -0.8 -0.5 0.1 -0.2 1.7 -2.8 1853 -1.600 -0.42 1 -0.6 -2.5 -2.4 -0.1 0.1 -0.8 0.0 0.7 -1.7 -0.5 -0.4 3.1 1854 -1.108 0.49 1 2.6 2.2 -0.2 0.8 0.2 0.4 0.0 0.3 -0.1 1.3 -1.0 -0.6 1855 -1.108 0.00 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1856 -1.475 -0.37 1 0.0 0.0 0.0 0.0 -0.7 0.3 -0.8 0.6 -0.1 -0.7 -1.3 -1.7 1857 -1.325 0.15 3 -0.2 0.6 0.0 0.1 -0.2 -0.2 0.5 0.0 -0.2 0.0 0.1 1.3 1858 -1.275 0.05 3 2.3 -2.1 1.2 -0.2 0.6 0.1 -0.8 -0.2 -0.7 0.2 1.4 -1.2 1859 -1.633 -0.36 4 -2.1 -1.1 -1.6 -0.5 -0.4 -0.2 -0.1 0.7 0.3 0.7 -0.5 0.5 1860 -1.358 0.27 4 0.8 0.2 2.2 0.2 0.4 0.7 0.4 0.0 0.7 -0.9 -0.5 -0.9 1861 -1.283 0.07 5 -0.6 0.5 0.4 0.4 0.1 0.5 -0.2 -1.0 -0.2 0.9 0.3 -0.2 1862 -1.000 0.28 6 0.6 0.0 0.0 -1.2 0.1 -0.7 1.2 0.4 0.6 -0.1 1.2 1.3 1863 -1.325 -0.32 7 -0.5 0.2 -0.3 0.9 0.4 0.3 -0.5 -0.3 -1.3 -0.7 -1.3 -0.8 1864 -1.458 -0.13 10 -0.4 -1.0 -0.6 -0.1 -0.2 -0.2 -0.1 0.1 0.7 0.0 0.3 -0.1 1865 -1.483 -0.02 11 -0.1 0.5 0.6 0.2 -0.9 0.0 -0.9 0.3 -0.3 0.2 0.7 -0.6 1866 -1.267 0.22 13 0.4 0.6 -0.7 -0.3 1.3 0.3 0.2 0.4 0.1 0.6 -0.7 0.4 1867 -1.275 -0.01 15 0.1 -0.4 0.0 0.1 -0.3 0.2 0.7 0.0 -0.1 0.1 -0.5 0.0 1868 -1.583 -0.31 15 -1.5 -0.9 0.5 -0.6 -0.2 -0.6 -0.8 -0.7 0.2 0.0 0.9 0.0 1869 -1.450 0.13 15 0.9 0.4 -0.2 0.1 -0.9 0.0 -0.1 1.0 -0.3 -0.5 0.2 1.0 1870 -1.425 0.02 16 0.6 1.1 -0.1 0.2 0.4 0.3 0.0 -0.9 -0.1 0.5 -0.5 -1.2 1871 -1.367 0.06 17 -0.2 -0.6 -0.5 0.1 0.8 -0.3 0.4 0.8 0.2 -0.8 -0.4 1.2 1872 -1.283 0.08 18 0.9 0.2 0.8 -0.1 -0.5 0.2 0.0 -1.5 0.3 0.0 0.8 -0.1 1873 -1.383 -0.10 19 -1.3 -0.7 -0.5 -0.1 0.7 0.5 -0.7 1.7 -0.2 1.0 -1.7 0.1 1874 -1.608 -0.23 19 0.6 0.0 0.1 0.9 -0.9 -1.7 0.0 -0.9 -1.0 -0.4 0.9 -0.3 1875 -1.350 0.26 17 0.4 0.4 0.2 0.2 0.1 0.8 0.7 0.6 0.4 0.0 -0.3 -0.4 1876 -1.125 0.23 19 0.0 0.3 1.1 -0.7 0.6 -0.7 -0.5 -0.4 0.5 0.4 0.8 1.3 1877 -1.142 -0.02 24 -0.1 0.6 -0.8 0.0 -0.4 0.3 0.5 0.6 -0.3 -0.6 0.3 -0.3 1878 -0.908 0.23 26 0.3 -0.6 1.0 0.9 0.0 -0.8 0.0 0.0 1.3 0.8 0.0 -0.1 1879 -1.358 -0.45 30 0.0 -0.2 -0.8 -0.8 -0.2 0.1 -0.7 -0.2 -0.6 -0.7 -0.7 -0.6 1880 -1.142 0.22 33 -0.3 0.6 0.5 0.3 0.4 0.1 0.0 0.4 0.0 -0.5 0.5 0.6 1881 -1.233 -0.09 36 -0.4 -0.4 -0.1 0.0 0.8 -0.1 0.2 -0.4 -0.4 0.1 -0.4 0.0 1882 -0.992 0.24 36 1.0 0.0 0.6 0.0 -0.4 0.2 -0.1 0.0 0.5 0.7 0.6 -0.2 1883 -1.233 -0.24 38 -0.5 -0.2 -0.8 0.0 -0.5 1.3 0.1 0.1 -1.1 -0.5 -0.7 -0.1 1884 -1.383 -0.15 38 -1.0 0.0 0.0 -0.1 -0.2 -0.7 -0.1 0.4 0.7 0.2 0.1 -1.1 1885 -1.167 0.22 41 0.4 -0.3 -0.7 -0.4 0.6 -0.5 0.0 -0.2 0.2 1.3 0.3 1.9 1886 -1.200 -0.03 41 0.9 0.0 0.3 0.7 -0.4 0.3 0.5 -0.2 0.0 -1.8 0.3 -1.0 1887 -1.275 -0.08 44 0.5 0.2 0.7 0.0 -0.4 -0.3 -0.1 0.0 -1.1 0.5 -1.1 0.2 1888 -1.083 0.19 47 -0.9 -0.1 -1.2 0.0 0.2 0.9 0.0 -0.2 1.0 0.1 1.8 0.7 1889 -0.792 0.29 49 0.4 0.9 1.6 0.3 1.1 0.0 0.0 0.2 -0.4 0.5 -0.8 -0.3 1890 -1.208 -0.42 49 0.0 -0.5 -0.4 -0.4 -0.5 0.3 -0.4 -0.1 0.3 -0.8 -1.2 -1.3 1891 -1.517 -0.31 51 -1.4 -1.1 -0.2 -0.6 0.0 -0.8 0.2 -0.2 -0.6 0.0 0.6 0.4 1892 -1.367 0.15 52 0.4 1.4 0.8 -0.3 -0.5 -0.1 0.1 0.5 0.1 -0.1 0.2 -0.7 1893 -1.358 0.01 53 -0.2 -0.5 -1.0 0.2 0.3 -0.3 0.1 0.0 0.1 0.8 -0.2 0.8 1894 -1.492 -0.13 58 0.6 -0.5 0.0 0.3 -0.9 0.4 -0.5 -0.4 -0.9 -0.3 0.5 0.1 1895 -1.367 0.12 61 -0.5 0.3 -0.2 0.0 0.3 0.0 -0.6 0.5 0.9 0.8 -0.4 0.4 1896 -1.342 0.03 60 1.6 0.2 0.1 -0.2 0.0 -0.7 0.1 -1.1 -0.3 0.1 0.2 0.3 1897 -1.283 0.06 64 -1.3 -0.1 -0.8 0.8 -0.1 1.1 1.3 0.2 0.6 -1.5 0.5 0.0 1898 -1.117 0.17 72 1.0 0.9 0.9 -0.7 -0.3 -0.5 -0.4 1.0 0.2 1.0 -0.8 -0.3 1899 -1.417 -0.30 75 -1.9 -0.1 0.2 0.5 0.2 -0.1 -0.7 -0.8 0.0 -1.2 -0.1 0.4 1900 -1.467 -0.05 80 1.4 0.1 -0.9 -1.0 0.0 0.5 0.0 -0.3 -1.4 0.9 0.6 -0.5 1901 -1.308 0.16 86 -0.5 -0.4 0.2 0.7 0.9 -1.1 -0.1 0.2 1.5 -0.3 0.7 0.1 1902 -1.433 -0.12 89 -0.1 -1.1 0.2 0.4 -0.2 0.6 0.7 0.0 -0.7 0.1 -0.4 -1.0 1903 -1.592 -0.16 101 0.5 0.4 0.0 -0.8 -0.5 0.0 -0.2 0.0 -0.3 -0.1 -0.8 -0.1 1904 -1.567 0.03 101 -0.5 -0.4 -0.8 1.2 0.4 -0.1 0.1 0.1 -0.5 -0.3 0.0 1.1 1905 -1.733 -0.17 106 0.8 0.0 0.4 -0.2 0.4 0.1 -0.1 -0.5 -0.9 -1.4 -0.4 -0.2 1906 -1.200 0.53 109 0.7 1.9 -0.1 0.3 0.1 1.0 0.5 0.3 1.0 1.5 -0.8 0.0 1907 -1.350 -0.15 186 -0.8 -0.6 -0.2 -0.7 -0.1 -0.5 0.0 0.4 0.5 0.0 0.6 -0.4 1908 -1.492 -0.14 192 1.4 0.0 0.0 0.3 -0.3 -1.6 -0.4 -0.7 -1.1 -0.7 0.8 0.6 1909 -1.650 -0.16 199 -1.8 -0.6 0.4 -1.1 -0.1 1.7 -0.1 0.5 0.4 0.6 -0.7 -1.1 1910 -1.225 0.42 210 0.6 1.0 0.0 1.6 0.8 0.1 0.7 0.7 1.2 -1.1 -0.4 -0.1 1911 -1.433 -0.21 215 -0.5 -1.0 -0.3 -1.1 -0.5 -1.3 0.0 -0.3 -0.5 0.9 1.4 0.7 1912 -1.025 0.41 219 0.8 2.0 1.2 0.5 0.0 1.4 0.1 0.0 -0.4 0.1 -1.0 0.2 1913 -1.300 -0.28 234 -0.3 -0.8 -1.2 0.5 -0.8 -0.8 0.2 -0.6 -0.1 0.4 -0.2 0.4 1914 -0.508 0.79 239 0.7 0.6 1.4 0.1 1.3 0.8 -0.6 1.1 0.7 0.8 2.2 0.4 1915 -0.775 -0.27 243 -0.5 0.5 -0.1 0.0 -0.6 0.2 1.4 -0.6 0.3 -1.4 -1.5 -0.9 1916 -1.292 -0.52 243 0.4 -0.8 -0.2 -0.9 0.5 -0.3 -0.9 -0.3 -0.3 -0.5 -2.2 -0.7 1917 -1.767 -0.47 245 -0.6 -2.2 -1.0 -0.7 -1.5 -0.6 0.2 -0.1 -0.5 0.2 0.8 0.3 1918 -1.400 0.37 245 -0.3 0.8 0.0 0.7 1.3 0.5 -1.1 0.5 0.1 0.1 1.3 0.5 1919 -0.750 0.65 247 0.8 1.5 0.7 1.2 0.8 0.4 0.6 -0.4 0.1 0.7 0.8 0.6 1920 -1.283 -0.53 244 -0.8 -0.6 -0.5 -1.2 -1.3 -0.3 0.3 -0.2 -0.3 -0.1 -0.6 -0.8 1921 -0.942 0.34 254 0.4 0.4 0.3 0.3 1.6 0.9 0.9 -0.3 0.4 -0.9 0.2 -0.1 1922 -1.158 -0.22 253 -0.5 -0.3 0.0 1.0 -1.0 -1.0 -1.5 0.0 -0.5 1.1 0.0 0.1 1923 -1.125 0.03 251 0.3 0.6 0.9 -0.2 0.8 0.0 0.1 0.0 -0.4 -0.8 -1.5 0.6 1924 -1.608 -0.48 253 -0.4 -1.6 -1.1 -1.8 -1.0 -0.5 0.7 0.7 0.8 0.0 0.2 -1.8 1925 -1.600 0.01 259 -0.3 0.2 0.0 0.9 0.1 0.6 -1.2 -0.8 -1.5 0.0 0.7 1.4 1926 -1.067 0.53 259 0.7 1.6 0.9 0.4 -0.7 0.0 1.3 0.8 1.6 0.6 0.0 -0.8 1927 -1.475 -0.41 260 0.2 -1.8 -0.7 -0.8 0.0 -0.5 -0.9 -0.6 -0.5 0.0 0.5 0.2 1928 -1.025 0.45 255 -0.5 0.6 0.6 1.3 0.0 -0.1 0.6 1.5 1.2 -0.4 -0.3 0.9 1929 -1.608 -0.58 259 1.0 0.6 -0.6 -1.7 0.0 -0.1 -1.4 -1.3 -1.6 0.0 -0.8 -1.1 1930 -1.092 0.52 262 -0.7 0.2 0.5 0.5 0.9 1.0 2.0 0.3 0.2 0.5 0.5 0.3 1931 -1.400 -0.31 268 0.0 -1.1 -0.3 -0.1 0.0 -0.1 -0.8 0.0 0.2 -1.0 -0.7 0.2 1932 -1.258 0.14 272 1.7 0.0 0.1 0.3 0.1 -0.6 -0.3 0.0 0.0 -0.1 0.8 -0.3 1933 -1.267 -0.01 274 -1.6 0.1 0.6 0.1 -0.3 0.8 0.6 -0.7 0.0 1.1 -0.6 -0.2 1934 -1.242 0.02 275 0.3 0.0 0.1 -0.4 0.5 -0.6 0.2 0.9 0.3 -1.0 0.0 0.0 1935 -1.392 -0.15 278 -0.4 0.0 -1.1 0.0 -1.2 -0.2 -0.5 0.2 -0.4 0.8 0.4 0.6 1936 -1.233 0.16 282 0.2 0.0 0.3 -0.1 0.7 -0.2 0.4 0.4 0.0 0.1 0.0 0.1 1937 -1.192 0.04 283 -0.4 -0.1 0.0 0.0 0.0 0.0 -0.6 -0.3 0.6 0.5 0.5 0.3 1938 -0.833 0.36 299 0.4 0.0 0.8 1.5 1.1 0.5 0.0 -0.6 -0.1 0.1 0.4 0.2 1939 -1.200 -0.37 314 1.3 0.9 -0.7 -0.5 -0.2 0.1 -0.4 -0.3 -0.7 -1.5 -1.4 -1.0 1940 -0.958 0.24 317 -0.8 -0.7 1.0 -0.5 -1.5 0.1 0.7 0.8 1.0 1.5 0.4 0.9 1941 -1.300 -0.34 321 -1.4 -0.4 -1.8 0.7 0.6 -0.2 0.1 -0.7 -0.2 -1.3 0.7 -0.2 1942 -0.875 0.42 310 1.8 0.0 1.1 0.0 0.8 0.8 0.2 1.1 0.1 0.2 -0.6 -0.4 1943 -1.517 -0.64 314 -1.1 0.3 0.3 -0.9 -1.2 -1.7 -0.8 -1.6 -0.4 0.1 -0.6 -0.1 1944 -1.208 0.31 315 1.0 -0.2 -1.0 -0.5 -0.5 0.5 0.6 0.9 0.7 0.3 1.7 0.2 1945 -1.133 0.07 321 -0.5 0.1 -0.3 1.1 0.8 1.5 -0.5 0.8 -0.9 -0.7 -0.9 0.4 1946 -1.342 -0.21 332 0.5 0.2 -0.4 -0.9 0.2 -2.1 0.6 -0.6 0.1 -0.2 0.2 -0.1 1947 -1.217 0.13 341 0.1 0.4 1.0 0.5 0.6 1.3 -0.1 -0.1 0.0 0.0 -1.3 -0.9 1948 -1.383 -0.17 349 -1.6 0.1 -0.7 -0.2 -1.3 -0.3 -0.4 0.4 0.3 0.5 0.6 0.6 1949 -1.550 -0.17 374 0.3 -1.1 0.3 -0.3 0.0 -0.9 0.3 -0.2 -0.2 0.3 -0.3 -0.2 1950 -1.267 0.28 391 0.3 0.1 0.1 0.7 0.7 0.8 0.6 -0.1 0.5 -0.4 0.0 0.1 1951 -1.350 -0.08 461 0.0 0.4 0.5 -0.7 -0.6 0.4 -0.8 -0.7 0.0 0.0 0.5 0.0 1952 -1.308 0.04 472 0.7 0.0 -0.3 0.5 0.2 -0.1 0.0 0.7 -0.3 0.0 -0.6 -0.3 1953 -1.325 -0.02 479 -1.0 -0.5 0.2 0.8 0.0 -0.3 0.1 -0.4 0.0 0.0 0.3 0.6 1954 -1.275 0.05 481 0.4 0.1 -0.4 -0.2 0.0 0.0 0.3 0.7 0.0 0.0 0.0 -0.3 1955 -1.325 -0.05 494 0.0 0.7 0.3 -0.1 -0.2 0.0 -0.5 0.0 0.3 0.0 -0.5 -0.6 1956 -1.625 -0.30 501 -0.4 0.1 0.0 -0.2 0.0 -0.3 0.1 -1.0 -1.0 -0.8 -0.2 0.1 1957 -1.267 0.36 405 0.0 -0.4 -0.4 0.3 0.1 1.3 -0.4 0.7 0.4 0.8 1.0 0.9 1958 -1.175 0.09 407 0.1 0.4 0.6 0.2 1.1 -0.7 0.6 0.1 -0.3 -0.3 0.0 -0.7 1959 -1.142 0.03 416 0.3 -0.1 0.1 -0.2 -1.0 0.0 0.2 0.0 0.6 0.0 0.5 0.0 1960 -1.500 -0.36 448 0.0 -0.3 -0.4 -0.4 -0.7 -0.8 -0.3 -0.7 -0.2 0.3 -1.1 0.3 1961 -1.242 0.26 457 -0.2 0.3 0.1 0.4 0.5 0.4 -0.2 0.1 0.4 0.4 0.7 0.2 1962 -1.275 -0.03 505 0.0 -0.2 -0.2 -0.2 0.0 0.8 0.5 0.1 -0.2 -0.7 0.1 -0.4 1963 -1.350 -0.08 504 -0.5 0.0 0.1 -0.3 0.4 -0.8 -0.5 0.1 0.1 0.5 -0.3 0.3 1964 -1.417 -0.07 508 0.4 0.0 0.0 0.4 -0.3 0.2 0.5 0.1 0.1 -1.0 -0.2 -1.0 1965 -1.200 0.22 634 -0.4 0.5 -0.1 -0.4 0.1 0.0 -0.7 0.2 0.5 1.3 0.4 1.2 1966 -1.358 -0.16 658 0.6 -0.3 0.1 0.5 -0.4 -0.1 0.2 -0.3 -0.6 -1.0 0.1 -0.7 1967 -1.200 0.16 669 -0.2 0.0 -0.6 0.2 0.5 0.7 0.2 -0.1 0.0 1.3 0.0 -0.1 1968 -1.292 -0.09 676 0.4 0.3 0.9 0.1 -0.7 -0.7 -0.2 0.0 -0.2 -0.8 -0.2 0.0 1969 -1.108 0.18 697 0.4 0.0 -0.1 -0.4 0.6 0.0 0.8 0.9 -0.6 0.4 0.1 0.1 1970 -1.217 -0.11 709 -0.6 0.0 -0.1 0.1 -0.4 0.4 -0.4 -0.7 0.4 -0.2 -0.1 0.3 1971 -1.325 -0.11 702 0.0 -0.1 0.2 -0.1 0.0 -0.7 -0.3 0.2 0.6 -0.2 -0.6 -0.3 1972 -1.058 0.27 713 -0.5 -0.2 -0.7 -0.1 0.5 0.4 0.2 0.3 0.5 0.4 1.1 1.3 1973 -0.750 0.31 705 1.5 0.4 0.5 0.7 0.4 0.2 1.0 0.0 -0.1 0.1 -0.2 -0.8 1974 -1.267 -0.52 707 -0.8 -0.7 0.4 -0.4 -0.5 -0.3 -0.7 -0.4 -0.8 -0.8 -0.8 -0.4 1975 -1.117 0.15 710 -0.5 0.4 -0.8 -0.3 0.2 0.0 0.7 0.0 0.8 0.0 0.9 0.4 1976 -1.392 -0.28 619 -0.1 -0.2 0.3 0.0 -0.5 0.0 -0.8 -0.3 -0.9 -0.2 -0.6 0.0 1977 -1.058 0.33 619 0.7 0.6 -0.1 0.0 0.0 -0.1 -0.3 0.9 0.0 1.3 0.8 0.2 1978 -1.200 -0.14 624 0.0 -0.2 0.7 0.3 0.6 0.1 0.0 -0.9 0.0 -0.9 -0.5 -0.9 1979 -0.892 0.31 622 0.8 0.0 -0.2 -0.2 -0.9 0.8 0.2 0.2 0.5 0.4 0.9 1.2 1980 -0.692 0.20 625 -0.8 -0.1 0.0 0.6 1.3 -0.3 0.0 0.7 0.8 0.3 0.1 -0.2 1981 -0.850 -0.16 617 0.8 0.3 -0.6 0.4 -0.6 -0.3 0.1 -0.5 0.1 -0.1 -1.2 -0.3 1982 -0.975 -0.12 585 -0.3 0.0 0.3 -0.9 -0.2 -0.7 -1.0 0.9 -1.1 -0.4 1.6 0.3 1983 -0.833 0.14 593 -0.6 0.9 0.7 -0.6 0.5 1.0 0.4 -0.4 0.9 0.6 -1.2 -0.5 1984 -1.342 -0.51 590 -0.8 -1.5 -1.5 0.4 -0.5 0.1 0.0 -0.4 -1.6 -0.4 0.4 -0.3 1985 -1.050 0.29 581 0.8 0.5 1.5 0.3 0.2 -0.5 0.2 0.0 0.4 0.0 0.0 0.1 1986 -1.100 -0.05 551 -0.3 -0.3 0.1 0.2 0.1 0.3 0.0 -0.4 0.4 -0.3 -0.2 -0.2 1987 -1.000 0.10 456 0.1 0.0 -1.2 0.1 -0.2 0.6 0.0 0.4 0.1 0.6 0.4 0.3 1988 -0.575 0.42 430 0.9 0.0 0.9 0.0 0.7 0.0 0.9 0.3 0.5 1.1 -0.3 0.1 1989 -1.042 -0.47 427 -0.9 0.3 0.0 0.0 -0.1 -1.0 -1.1 -1.2 -0.6 -1.0 0.1 -0.1 1990 -0.825 0.22 523 0.3 -0.1 0.2 -0.1 -0.1 0.1 0.4 0.2 -0.1 0.2 0.7 0.9 1991 -0.708 0.12 568 0.3 0.5 -0.5 -0.1 0.0 1.4 0.0 0.4 0.2 0.7 -0.7 -0.8 1992 -1.075 -0.37 530 -1.0 -0.5 0.3 0.2 -0.2 -1.3 0.2 -0.2 -0.7 -1.0 -0.2 0.0 1993 -1.058 0.02 126 0.0 -0.2 -0.4 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.5 0.2 1994 -1.025 0.03 123 0.3 0.3 0.0 0.0 0.0 -0.1 -0.4 -0.3 0.0 0.2 0.0 0.4 1995 -1.017 0.01 124 -0.2 -0.2 0.0 -0.1 -0.1 0.2 0.0 0.5 0.3 0.1 0.1 -0.5 1996 -1.000 0.02 132 -0.1 0.0 0.0 0.0 0.0 0.2 0.3 -0.2 0.0 0.1 -0.1 0.0 1997 -1.050 -0.05 132 -0.1 0.2 -0.2 0.0 -0.1 -0.5 -0.4 -0.2 0.0 0.0 0.2 0.5 1998 -0.633 0.42 122 0.6 0.4 0.8 0.4 0.7 0.4 0.5 0.7 0.4 0.1 0.0 0.0 1999 -0.908 -0.28 123 0.0 -0.5 -0.3 -0.5 -0.3 -0.2 0.0 -0.3 -0.2 -0.1 -0.4 -0.5 2000 -0.967 -0.06 128 -0.5 0.0 -0.2 0.1 -0.5 -0.2 0.0 -0.1 0.1 -0.1 0.3 0.4 2001 -0.900 0.07 125 0.5 0.0 0.0 0.2 0.1 0.4 0.0 0.2 0.0 -0.1 -0.2 -0.3 2002 -0.758 0.14 125 -0.2 -0.2 0.2 0.2 0.4 0.1 0.1 -0.1 -0.1 0.4 0.5 0.4 2003 -0.833 -0.07 125 0.1 0.2 -0.1 -0.2 0.0 0.0 -0.1 0.0 -0.1 -0.5 -0.2 0.0 2004 -0.858 -0.02 131 0.0 0.1 0.0 0.0 -0.4 -0.1 -0.1 -0.1 -0.2 0.6 0.0 -0.1 2005 -0.692 0.17 138 0.1 0.0 0.2 0.3 0.3 0.0 0.3 0.1 0.3 0.0 0.1 0.3 2006 -0.892 -0.20 140 0.1 -0.1 0.0 -0.8 -0.7 -0.6 -0.3 0.1 0.2 0.0 0.1 -0.4 2007 -0.750 0.14 144 -0.2 0.3 0.0 0.4 1.0 0.1 0.0 0.1 0.0 0.1 -0.1 0.0 2008 -0.817 -0.07 159 0.2 -0.4 0.0 -0.3 -0.5 0.4 0.0 -0.4 0.1 0.1 0.0 0.0 2009 -0.575 0.24 160 0.0 0.3 0.0 0.3 -0.1 0.0 0.2 1.0 0.2 -0.2 0.8 0.4 2010 -0.558 0.02 121 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 5 From input file data/EMS.inv11.M1800.dt
This is about the best most unbiased approach I could figure out. There is one more enhancement “in the works” but it ought not to have much impact. (That is to start each thermometer as the ‘offset from that thermometer average of all data’ so as to reduce start of time series bias – for now we just depend on the average of a large number of start years for the set of thermometers to smooth that out some.)
What About the U.S.A. ?
While I personally find the Pacific Basin much more interesting than the USA, I’ll put the preliminary USA report here for all the USA centric folks (and since there is a current and active debate about strangling our economy due to our “warming” that isn’t happening).
One of the things that gives me some confidence in this approach is that we can see known historical events showing in the report. The 1930’s are warm. 1998 is warm. 1816-1817 (“Eighteen Hundred and Froze to Death”) is cold, as are the 1960s and 1970’s and we also see a cooling 2007-2008 (despite the big ‘pruning’ of thermometers of record in 2006):
Produced from input file: ./DTemps/Temps.M425 Thermometer Records, Average of Monthly dT/dt, Yearly running total by Year Across Month, with a count of thermometer records in that year ----------------------------------------------------------------------------------- YEAR dT dT/yr Count JAN FEB MAR APR MAY JUN JULY AUG SEPT OCT NOV DEC ----------------------------------------------------------------------------------- 1800 0.000 0.00 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1801 0.667 0.67 4 1.2 2.8 2.5 -2.5 1.3 -0.1 -0.3 -0.5 1.3 1.6 0.8 -0.1 1802 0.775 0.11 4 3.7 -0.2 -0.8 2.0 -2.0 -0.3 -0.6 0.9 -0.5 -0.3 0.4 -1.0 1803 0.683 -0.09 4 -4.0 0.5 -0.3 0.0 -0.6 1.0 1.1 0.2 -1.4 0.4 -0.8 2.8 1804 0.258 -0.43 3 -2.1 -1.9 -1.4 -1.6 3.2 -0.6 -0.8 0.0 3.3 -1.4 2.3 -4.1 1805 1.233 0.98 2 -0.2 0.9 3.4 2.6 -0.9 -0.4 0.9 1.0 0.4 -0.2 -1.6 5.8 1806 -0.067 -1.30 2 1.8 1.4 -5.3 -4.0 0.3 -0.1 -2.2 -2.5 -1.8 0.9 0.6 -4.7 1807 -0.242 -0.17 2 -1.8 -3.8 0.5 1.2 -1.9 -1.8 2.5 1.3 -1.3 0.8 -1.3 3.5 1808 0.292 0.53 2 1.2 2.9 3.4 1.6 0.0 1.9 -1.3 -1.7 0.3 -1.6 1.8 -2.1 1809 -0.383 -0.68 2 -1.8 -3.7 -2.1 -0.5 1.2 -0.8 -2.3 0.1 -1.6 4.9 -3.0 1.5 1810 0.300 0.68 2 2.4 4.4 0.2 1.2 0.5 0.8 0.9 0.8 2.5 -3.9 1.8 -3.4 1811 0.458 0.16 2 0.1 -3.0 3.3 -1.8 -1.3 -0.1 0.8 -0.4 0.0 2.5 1.2 0.6 1812 -0.833 -1.29 3 -1.8 0.0 -3.8 -0.3 -1.9 -1.4 -0.8 -0.5 -1.8 -2.0 -0.7 -0.5 1813 -0.150 0.68 4 0.2 0.1 0.3 0.7 0.8 0.7 0.5 1.3 2.2 0.0 1.1 0.3 1814 0.075 0.22 3 0.2 1.6 1.1 0.2 3.4 -0.4 -0.4 -1.3 -1.9 0.7 -0.2 -0.3 1815 -0.592 -0.67 2 -0.3 -3.3 1.5 -1.7 -4.4 0.1 1.4 -1.2 -0.6 -0.6 0.7 0.4 1816 -1.017 -0.42 3 1.2 1.7 -1.0 -0.9 0.0 -2.4 -3.2 -0.6 -2.4 0.8 0.7 1.0 1817 -1.133 -0.12 4 -0.8 -3.6 0.0 0.3 0.6 0.4 1.0 0.7 1.8 -1.4 -0.6 0.2 1818 -1.100 0.03 3 0.8 0.6 0.9 -3.2 0.3 2.4 1.6 -0.1 -1.1 0.6 0.5 -2.9 1819 -0.292 0.81 4 2.0 4.2 -1.6 1.1 0.0 0.0 -0.5 1.1 2.2 -0.3 -0.2 1.7 1820 -0.608 -0.32 9 -1.6 0.0 0.7 0.3 -0.4 -0.1 0.6 -0.2 -0.2 0.0 -1.6 -1.3 1821 -0.825 -0.22 10 -0.4 -1.2 -0.3 -3.3 0.2 0.5 -1.6 1.5 0.1 0.9 0.8 0.2 1822 -0.225 0.60 13 1.0 -0.7 2.1 1.4 1.1 -0.3 1.3 -0.7 0.3 0.3 1.4 0.0 1823 -0.775 -0.55 14 2.3 -2.9 -1.6 1.1 -1.4 -0.2 0.0 0.1 -2.0 -0.7 -2.7 1.4 1824 -0.208 0.57 17 1.7 2.5 -0.2 -0.6 -0.2 -0.2 -0.1 -0.7 0.8 0.5 1.4 1.9 1825 0.475 0.68 18 -1.1 1.9 2.8 1.1 0.8 1.8 1.3 0.8 -0.3 0.7 0.0 -1.6 1826 0.333 -0.14 17 -0.5 -0.1 -1.1 -2.3 2.6 -0.4 -1.3 0.0 0.5 -0.1 0.4 0.6 1827 0.208 -0.12 21 -1.7 1.1 0.3 2.4 -2.3 -0.7 0.1 0.0 0.0 0.0 -1.2 0.5 1828 1.083 0.87 24 3.9 2.1 0.5 -2.6 0.2 1.8 -0.1 1.0 -0.3 -0.3 2.3 2.0 1829 -0.167 -1.25 29 -2.1 -6.1 -2.7 0.8 0.6 -1.2 -0.7 -0.9 -1.4 0.3 -1.9 0.3 1830 0.733 0.90 30 -0.5 2.2 2.5 2.1 -1.4 -0.7 2.0 0.2 1.1 0.8 4.3 -1.8 1831 -0.433 -1.17 31 -1.8 -1.9 0.5 -1.3 0.5 1.6 -1.1 0.0 0.2 -0.5 -3.5 -6.7 1832 -0.275 0.16 36 2.2 0.9 -1.3 -1.1 -1.3 -1.9 -0.8 -0.9 -0.2 -0.2 0.4 6.1 1833 0.017 0.29 34 2.0 0.2 -1.2 2.2 2.2 -0.7 0.5 -0.3 0.6 -1.2 -0.9 0.1 1834 0.075 0.06 36 -3.6 3.9 1.5 -0.4 -1.9 0.8 1.1 1.0 -0.3 -0.1 0.1 -1.4 1835 -1.008 -1.08 34 2.1 -6.7 -1.8 -2.0 0.6 0.1 -1.8 -1.2 -1.9 1.7 -0.1 -2.0 1836 -1.667 -0.66 38 -0.8 -0.5 -1.9 -0.3 0.2 -0.8 0.0 -0.9 1.1 -3.7 -0.8 0.5 1837 -1.292 0.37 41 -1.5 2.6 0.9 -1.0 -1.7 0.1 -0.3 0.7 -0.4 2.1 1.9 1.1 1838 -1.267 0.03 38 3.8 -4.6 2.4 -0.1 -0.4 1.8 2.1 1.3 0.9 -1.0 -3.4 -2.5 1839 -0.442 0.82 39 -1.4 5.5 -0.9 3.6 1.6 -2.5 -0.8 -0.7 -0.5 2.9 0.9 2.2 1840 -0.492 -0.05 42 -3.0 1.6 1.0 -0.1 0.6 1.2 -0.3 0.6 -0.4 -1.7 0.8 -0.9 1841 -0.617 -0.12 43 4.0 -3.4 -1.2 -2.6 -1.5 0.9 -0.1 0.0 1.7 -1.5 0.2 2.0 1842 -0.300 0.32 44 0.7 3.3 3.4 2.7 -0.4 -2.3 -0.2 -0.8 -1.2 1.5 -1.4 -1.5 1843 -1.075 -0.77 48 0.6 -5.5 -7.0 -1.4 0.6 0.8 -0.1 0.7 1.3 -1.4 0.5 1.6 1844 -0.542 0.53 50 -4.7 3.6 4.6 2.9 1.4 0.1 0.0 -0.7 -1.0 0.5 0.5 -0.8 1845 -0.325 0.22 47 4.3 0.6 1.1 -2.0 -1.7 0.2 0.4 1.0 0.0 0.8 0.6 -2.7 1846 0.125 0.45 43 -0.5 -2.8 -0.6 0.2 1.9 -0.5 -0.1 0.4 2.5 -0.4 1.7 3.6 1847 -0.667 -0.79 43 -1.6 1.6 -2.8 -1.7 -1.6 0.0 0.4 -1.4 -2.2 -0.2 -0.5 0.5 1848 -0.458 0.21 42 2.3 0.3 1.1 0.5 1.5 0.8 -1.2 0.3 -1.5 0.6 -3.2 1.0 1849 -0.700 -0.24 48 -2.8 -2.5 1.2 -0.9 -1.8 0.3 0.3 0.0 1.0 -0.1 4.5 -2.1 1850 -0.525 0.18 48 2.8 3.0 -1.2 -0.6 -0.6 -0.2 0.7 0.1 0.2 0.5 -1.6 -1.0 1851 -0.492 0.03 54 -0.7 0.8 1.7 1.1 1.3 -0.6 -0.5 -0.6 0.4 0.6 -2.1 -1.0 1852 -0.617 -0.12 56 -3.0 -0.7 -1.2 -1.4 0.4 0.4 0.3 0.0 -1.0 0.5 0.5 3.7 1853 -0.325 0.29 46 2.4 -0.9 0.2 1.9 -0.4 0.8 -0.4 0.5 0.7 -1.6 1.9 -1.6 1854 -0.267 0.06 59 -0.5 -0.1 0.3 -0.6 0.1 -0.5 1.1 0.3 0.4 1.4 -0.6 -0.6 1855 -0.675 -0.41 64 1.3 -2.3 -1.6 1.0 0.0 -0.9 -1.0 -1.0 -0.2 -1.7 0.9 0.6 1856 -1.308 -0.63 74 -4.2 -0.2 -1.5 0.0 -0.7 1.9 0.6 -0.2 -0.8 0.3 -1.1 -1.7 1857 -1.142 0.17 82 -0.7 4.7 1.1 -3.3 -0.7 -1.6 -0.8 0.3 0.2 -0.5 -0.3 3.6 1858 -0.567 0.57 81 6.8 -4.1 1.1 2.4 0.2 1.2 0.1 0.1 -0.1 1.3 -1.1 -1.0 1859 -0.683 -0.12 77 -2.3 3.2 1.6 -0.9 1.6 -1.2 -0.3 -0.1 -0.6 -2.0 2.4 -2.8 1860 -0.350 0.33 59 0.3 -0.6 0.0 1.4 0.3 0.7 0.2 0.3 0.0 1.2 -0.6 0.8 1861 -0.417 -0.07 71 -1.4 1.1 -1.5 0.3 -1.8 0.1 -0.5 -0.4 0.3 0.5 0.0 2.5 1862 -0.775 -0.36 64 -0.1 -3.0 -0.7 -1.2 0.9 -1.1 0.3 0.7 0.6 0.0 -0.5 -0.2 1863 -0.683 0.09 67 2.7 1.4 -0.6 0.6 0.8 -0.4 0.0 -0.1 -1.1 -1.6 0.6 -1.2 1864 -0.583 0.10 77 -1.6 0.9 0.7 -0.6 0.2 1.2 0.8 0.3 0.3 0.0 -0.4 -0.6 1865 -0.292 0.29 75 -1.5 -0.7 1.3 1.4 -0.4 1.0 -1.4 -0.9 2.7 0.7 0.9 0.4 1866 -0.667 -0.38 82 0.7 -0.9 -1.8 0.7 -1.1 -1.1 1.5 -1.0 -2.8 0.9 0.2 0.2 1867 -0.742 -0.07 88 -1.4 2.1 -1.8 -1.3 -1.3 0.7 -1.1 1.7 1.1 0.0 0.2 0.2 1868 -1.058 -0.32 97 -0.3 -3.0 4.2 -1.2 1.3 -0.7 2.2 -0.7 -1.7 -1.3 -1.3 -1.3 1869 -0.850 0.21 103 4.0 2.2 -2.8 0.9 -0.2 -0.5 -2.1 0.5 1.1 -1.4 -0.9 1.7 1870 -0.108 0.74 125 0.2 -0.4 -0.2 1.0 1.5 1.5 1.2 0.0 0.8 2.5 1.6 -0.8 1871 -0.183 -0.07 146 -0.6 0.2 3.0 0.4 -0.2 -0.1 -0.9 0.6 -1.1 0.2 -1.9 -0.5 1872 -0.850 -0.67 163 -1.1 -0.8 -3.9 -0.8 0.0 -0.1 0.5 0.0 0.9 -0.8 -0.5 -1.4 1873 -0.850 0.00 171 -0.7 -0.6 1.5 -0.9 -0.9 0.0 -0.2 -0.5 -0.4 -0.8 0.7 2.8 1874 -0.308 0.54 172 2.2 0.6 0.2 -1.5 1.0 0.1 0.3 0.0 1.0 1.2 1.1 0.3 1875 -1.208 -0.90 176 -4.2 -3.1 -1.4 0.9 -0.2 -0.8 -0.4 -0.4 -1.2 -0.6 -0.9 1.5 1876 -0.592 0.62 177 5.6 4.2 0.1 1.1 -0.3 0.7 0.5 0.7 0.0 -0.6 0.8 -5.4 1877 0.000 0.59 179 -3.1 1.5 1.0 0.0 -0.4 -0.6 -0.2 0.0 1.1 1.4 0.3 6.1 1878 0.233 0.23 192 1.7 -0.4 3.3 1.9 0.1 -0.5 0.5 0.4 -0.4 -0.2 0.5 -4.1 1879 -0.158 -0.39 201 -2.0 -2.2 -1.5 -1.6 0.9 0.2 -0.4 -0.9 -0.6 2.1 -0.5 1.8 1880 -0.292 -0.13 203 5.2 1.7 -1.8 0.2 1.0 0.6 -0.6 0.0 0.2 -2.6 -3.5 -2.0 1881 0.000 0.29 212 -6.2 -1.5 0.0 -0.6 0.0 -0.5 0.6 0.9 1.9 1.5 3.0 4.4 1882 -0.167 -0.17 259 2.3 2.1 1.1 0.2 -2.6 0.0 -1.0 -0.6 -1.1 0.1 -0.1 -2.4 1883 -0.675 -0.51 277 -2.3 -2.3 -1.6 0.0 0.3 0.4 0.5 -0.6 -0.6 -1.2 0.6 0.7 1884 -0.558 0.12 316 0.0 0.6 0.5 -0.6 0.9 -0.6 -0.6 0.0 1.2 1.3 -0.2 -1.1 1885 -0.725 -0.17 335 0.3 -2.0 -0.8 0.7 -0.3 0.0 0.8 0.0 -0.8 -1.8 0.4 1.5 1886 -0.508 0.22 356 -0.7 1.8 0.6 0.6 1.0 0.0 -0.2 0.8 0.6 1.0 -1.1 -1.8 1887 -0.375 0.13 386 0.9 0.1 0.4 -0.4 0.8 0.4 0.7 -0.6 -0.6 -1.1 0.4 0.6 1888 -0.575 -0.20 441 -1.2 0.3 -1.3 0.5 -1.9 -0.1 -0.6 0.2 -0.1 -0.1 0.5 1.4 1889 -0.092 0.48 511 2.9 -1.3 2.3 0.3 0.5 -0.5 -0.2 -0.2 0.0 0.0 -0.4 2.4 1890 -0.050 0.04 536 0.5 2.4 -1.8 -0.4 -0.2 1.1 0.2 -0.3 0.0 0.4 1.4 -2.8 1891 -0.217 -0.17 595 0.2 -0.9 -0.6 0.1 -0.2 -0.7 -1.3 0.4 1.4 0.0 -1.5 1.1 1892 -0.508 -0.29 662 -2.0 1.0 0.6 -1.1 -0.4 0.1 0.7 0.2 -0.6 0.6 0.0 -2.6 1893 -0.692 -0.18 728 -1.1 -2.2 0.0 0.0 0.1 0.1 0.5 -0.3 -0.2 -0.3 0.0 1.2 1894 0.042 0.73 765 2.5 -0.5 2.4 1.0 1.0 0.0 0.0 0.5 0.6 0.5 0.2 0.6 1895 -0.608 -0.65 822 -1.8 -1.5 -1.7 0.3 -0.1 0.0 -1.0 0.0 0.5 -1.8 0.0 -0.7 1896 0.000 0.61 851 2.0 3.8 -1.1 0.3 1.5 0.0 1.0 0.2 -1.8 0.6 -0.1 0.9 1897 -0.125 -0.12 886 -1.3 -0.2 1.1 -1.1 -1.6 -0.5 0.1 -0.7 1.9 2.1 0.5 -1.8 1898 -0.100 0.03 909 1.8 0.4 1.4 -0.6 0.0 0.7 -0.2 0.8 -0.2 -1.8 -1.1 -0.9 1899 -0.367 -0.27 927 -0.9 -4.6 -2.7 0.5 0.2 -0.1 -0.2 -0.1 -1.0 1.5 3.1 1.1 1900 0.350 0.72 952 1.7 1.9 1.0 0.5 0.5 0.1 0.1 0.9 0.9 1.3 -1.5 1.2 1901 -0.150 -0.50 966 -0.8 -0.4 0.6 -1.3 -0.6 -0.1 1.7 -0.5 -1.1 -1.1 -0.7 -1.7 1902 -0.192 -0.04 983 -0.9 0.5 1.0 0.5 0.9 -0.8 -2.0 -1.0 -0.6 -0.3 2.3 -0.1 1903 -0.575 -0.38 1016 0.3 0.2 0.7 0.0 -0.9 -1.1 -0.2 -0.2 0.2 -0.3 -2.7 -0.6 1904 -0.650 -0.07 1054 -2.0 -0.2 -1.2 -1.0 0.0 0.9 -0.4 -0.1 0.9 -0.1 1.4 0.9 1905 -0.458 0.19 1067 -0.2 -1.9 1.9 1.1 -0.2 0.5 0.3 0.8 0.5 -0.8 -0.1 0.4 1906 -0.033 0.42 1097 3.9 3.4 -4.7 1.2 0.0 -0.2 0.0 0.2 0.5 0.2 -0.5 1.1 1907 -0.325 -0.29 1127 -1.1 0.3 4.9 -3.3 -2.1 -0.9 0.3 -0.8 -1.2 0.1 0.0 0.3 1908 0.150 0.47 1150 0.4 -0.5 -0.7 3.0 1.8 0.6 0.2 0.0 0.7 -0.2 0.9 -0.5 1909 -0.250 -0.40 1179 -0.1 1.5 -1.9 -1.7 -0.6 0.8 -0.5 1.0 -0.8 0.0 1.4 -3.9 1910 0.117 0.37 1191 -1.1 -3.0 4.8 2.2 0.1 -0.5 0.8 -0.9 0.6 1.8 -2.7 2.3 1911 0.217 0.10 1222 1.5 2.5 -3.1 -1.8 1.8 1.6 -0.3 0.0 0.4 -1.4 -1.5 1.5 1912 -0.742 -0.96 1234 -4.7 -2.0 -3.9 0.6 -0.5 -2.1 -0.2 -0.4 -1.3 0.3 2.5 0.2 1913 0.042 0.78 1252 4.2 -0.3 1.8 0.1 -0.4 1.0 0.4 1.7 0.0 -0.8 1.5 0.2 1914 -0.033 -0.07 1264 1.5 -0.4 0.3 -0.3 0.7 0.5 0.3 -0.8 0.2 1.8 -0.9 -3.8 1915 -0.167 -0.13 1276 -2.9 3.8 -1.7 2.1 -1.6 -1.9 -1.5 -1.1 0.3 0.0 0.0 2.9 1916 -0.517 -0.35 1297 0.1 -2.1 2.0 -2.5 0.7 -0.1 1.8 1.3 -0.7 -1.5 -1.3 -1.9 1917 -1.100 -0.58 1313 -0.1 -1.3 -0.9 -0.7 -2.4 0.4 -0.2 -0.7 0.0 -1.5 1.0 -0.6 1918 -0.017 1.08 1326 -3.0 2.0 3.3 0.0 3.3 1.8 -0.9 1.1 -1.1 3.4 -0.7 3.8 1919 -0.017 0.00 1334 4.8 -0.2 -1.9 1.1 -0.8 -0.3 1.1 -0.6 2.2 -1.2 -0.7 -3.5 1920 -0.367 -0.35 1342 -1.9 0.0 -0.5 -2.1 -0.4 -1.0 -1.0 -0.6 0.0 0.8 0.0 2.5 1921 1.067 1.43 1350 2.9 2.0 3.3 2.5 0.7 1.5 1.3 0.5 0.8 -0.2 1.2 0.7 1922 0.233 -0.83 1353 -3.4 -2.2 -3.1 -0.4 0.6 -0.1 -1.1 0.3 0.0 0.1 0.0 -0.7 1923 -0.017 -0.25 1360 3.4 -1.6 -1.4 -0.4 -1.2 -0.9 0.4 -0.6 -1.0 -1.9 0.2 2.0 1924 -0.700 -0.68 1360 -3.9 2.5 -0.4 0.1 -0.9 -0.1 -1.1 0.3 -1.5 1.8 -0.2 -4.8 1925 0.308 1.01 1367 1.1 1.9 3.3 2.3 1.0 1.0 1.1 -0.1 2.6 -3.4 -0.9 2.2 1926 0.017 -0.29 1370 0.9 -0.4 -2.5 -2.6 0.9 -1.3 -0.1 0.5 -1.5 3.1 0.0 -0.5 1927 0.175 0.16 1376 0.2 0.5 1.7 0.8 -0.8 -0.4 -0.4 -1.9 0.5 0.8 2.0 -1.1 1928 0.067 -0.11 1383 0.4 -1.9 0.0 -1.7 0.8 -0.6 0.4 1.6 -1.3 -0.3 -1.1 2.4 1929 -0.450 -0.52 1386 -2.9 -3.7 0.8 1.8 -1.1 0.9 0.0 0.0 0.4 -0.8 -1.6 0.0 1930 0.192 0.64 1391 -1.1 6.5 -1.8 1.2 0.5 0.5 0.8 0.4 1.2 -0.9 1.3 -0.9 1931 1.083 0.89 1398 4.7 -0.8 -0.6 -1.3 -0.1 1.1 0.4 -0.4 1.2 2.5 1.6 2.4 1932 0.133 -0.95 1404 -0.1 -0.5 -1.1 0.0 0.6 -0.6 -0.9 0.3 -2.0 -1.9 -2.2 -3.0 1933 0.658 0.53 1408 1.2 -3.4 2.2 -0.5 0.0 1.2 0.3 -0.4 1.5 0.8 0.7 2.7 1934 1.067 0.41 1406 -0.2 1.0 0.2 1.5 2.1 -0.2 0.9 0.7 -1.8 0.8 1.7 -1.8 1935 0.058 -1.01 1407 -2.0 1.6 1.2 -1.9 -3.7 -2.0 -0.5 -0.1 0.2 -1.2 -2.6 -1.1 1936 0.208 0.15 1412 -2.1 -5.8 -0.1 0.1 3.3 1.5 0.7 1.0 1.0 0.0 -0.2 2.4 1937 -0.033 -0.24 1414 0.1 3.8 -2.6 0.1 -0.9 -0.7 -1.2 0.0 -0.7 -0.1 0.5 -1.2 1938 0.850 0.88 1415 2.2 2.3 3.5 1.1 -0.9 -0.2 -0.2 -0.3 0.5 1.6 0.1 0.9 1939 0.800 -0.05 1416 1.3 -2.7 -1.6 -0.6 1.4 0.2 0.3 -0.7 0.6 -0.9 0.5 1.6 1940 -0.025 -0.83 1415 -5.8 1.3 -0.9 -0.4 -1.1 0.1 -0.2 -0.4 -1.2 0.5 -1.3 -0.5 1941 0.642 0.67 1423 4.6 -0.3 -0.9 1.7 1.1 -0.5 0.0 0.2 0.0 0.0 2.0 0.1 1942 0.200 -0.44 1430 -0.8 -1.0 1.6 0.2 -1.4 0.0 0.0 -0.2 -0.7 -0.5 -0.1 -2.4 1943 0.175 -0.03 1427 -0.5 2.5 -1.7 -0.9 0.0 0.7 0.1 0.8 0.0 -0.6 -1.0 0.3 1944 0.250 0.07 1435 1.9 -0.5 0.0 -1.5 1.5 -0.3 -0.6 -0.5 0.7 0.7 0.5 -1.0 1945 0.100 -0.15 1482 -1.4 0.1 4.1 1.0 -2.5 -1.5 -0.1 -0.1 0.0 -0.6 0.0 -0.8 1946 0.717 0.62 1485 0.9 -0.1 0.4 1.5 0.4 1.0 0.4 -0.7 -0.3 0.1 0.2 3.6 1947 0.175 -0.54 1507 0.1 -2.1 -4.5 -1.4 0.4 -0.7 -0.5 2.0 0.9 2.5 -1.9 -1.3 1948 -0.017 -0.19 1624 -2.3 0.2 0.4 1.0 0.0 0.8 0.3 -1.2 -0.2 -2.9 1.7 -0.1 1949 0.308 0.33 1754 0.3 0.5 0.8 -0.7 0.8 0.4 0.5 0.1 -1.2 0.9 1.1 0.4 1950 -0.208 -0.52 1763 1.9 0.9 -1.2 -1.7 -1.0 -0.7 -1.6 -1.0 0.0 1.2 -2.2 -0.8 1951 -0.267 -0.06 1791 -0.7 -0.1 0.0 0.7 0.5 -0.5 1.1 0.8 0.2 -1.4 -1.1 -0.2 1952 0.317 0.58 1804 0.9 0.7 -0.2 0.8 -0.3 2.1 0.5 0.3 0.8 -1.0 1.4 1.0 1953 0.875 0.56 1818 1.8 0.2 2.6 -1.2 0.0 -0.2 -0.1 -0.1 0.1 1.9 1.5 0.2 1954 0.708 -0.17 1828 -2.7 2.1 -2.0 2.5 -1.0 -0.6 0.5 0.0 0.2 -0.6 0.0 -0.4 1955 0.008 -0.70 1755 -0.4 -4.6 0.4 -0.4 1.6 -1.4 0.0 0.9 -0.3 -0.1 -2.9 -1.2 1956 0.217 0.21 1757 0.3 0.5 0.0 -1.9 0.0 1.8 -1.0 -1.0 -0.6 0.6 1.1 2.7 1957 0.258 0.04 1765 -1.6 2.6 0.5 1.0 -0.6 -0.3 0.6 -0.1 0.0 -2.1 0.3 0.2 1958 0.017 -0.24 1769 2.1 -3.4 -1.6 -0.2 0.9 -0.7 -0.6 0.7 0.4 1.3 1.0 -2.8 1959 0.217 0.20 1767 -1.1 0.9 1.3 0.4 -0.2 1.0 0.2 0.2 0.0 -0.4 -2.2 2.3 1960 -0.208 -0.42 1763 0.5 -0.8 -3.2 0.5 -1.0 -0.5 0.0 -0.6 0.5 0.5 1.9 -2.9 1961 -0.008 0.20 1761 -0.4 2.9 4.3 -2.2 -0.4 0.1 -0.1 0.1 -0.9 -0.3 -0.9 0.2 1962 -0.058 -0.05 1801 -1.0 -1.2 -2.5 1.5 2.0 -0.5 -0.6 -0.2 -0.4 0.9 0.7 0.7 1963 0.017 0.07 1850 -1.4 -1.1 2.8 0.2 -0.8 0.3 0.6 -0.2 0.9 1.7 0.6 -2.7 1964 -0.108 -0.12 1841 3.6 -0.4 -2.3 -0.3 0.4 -0.2 0.4 -0.6 -0.9 -3.1 -0.5 2.4 1965 -0.142 -0.03 1835 -0.4 0.1 -1.6 0.2 0.0 -0.7 -1.0 0.3 -0.5 0.5 0.7 2.0 1966 -0.283 -0.14 1830 -2.4 -0.2 3.4 -1.0 -1.1 0.6 1.3 -0.1 0.7 -0.8 -0.5 -1.6 1967 -0.142 0.14 1823 3.6 0.1 0.2 0.8 -1.1 0.0 -1.3 0.0 -0.2 0.5 -1.1 0.2 1968 -0.317 -0.17 1822 -2.4 0.0 0.2 0.0 0.4 0.2 0.4 0.1 0.2 0.4 0.0 -1.6 1969 -0.300 0.02 1813 0.0 0.4 -3.7 0.7 1.6 -0.6 0.5 0.7 0.5 -1.4 0.0 1.5 1970 -0.175 0.13 1797 -1.3 1.0 1.1 -1.2 0.1 0.7 0.0 0.2 0.0 0.6 0.1 0.2 1971 -0.158 0.02 1693 1.1 -0.8 0.1 0.0 -1.7 0.5 -0.8 -0.6 0.0 1.6 0.0 0.8 1972 -0.417 -0.26 1689 0.8 -0.2 1.9 0.0 1.2 -1.0 0.0 0.0 -0.2 -2.1 -1.2 -2.3 1973 0.217 0.63 1685 0.0 0.2 1.3 -0.3 -0.8 0.8 0.5 0.4 0.0 2.1 1.9 1.5 1974 0.058 -0.16 1679 0.7 0.4 -0.6 1.2 0.5 -0.7 0.3 -1.0 -1.2 -1.4 -0.2 0.1 1975 -0.208 -0.27 1670 0.5 -0.6 -2.8 -2.5 0.6 0.0 -0.2 0.6 0.1 0.7 0.4 0.0 1976 -0.383 -0.18 1669 -1.7 3.3 2.2 2.6 -1.3 0.1 -0.3 -0.6 0.7 -2.6 -2.7 -1.8 1977 0.150 0.53 1660 -3.6 -1.9 0.6 1.1 1.9 0.8 0.9 0.5 0.9 1.7 2.4 1.1 1978 -0.575 -0.73 1660 1.5 -4.4 -1.8 -1.4 -1.2 -0.3 -0.4 0.1 0.1 0.2 -0.3 -0.8 1979 -0.575 -0.00 1657 -1.4 -0.2 1.0 -0.6 -0.1 -0.5 -0.3 -0.4 -0.1 0.5 -0.4 2.5 1980 0.042 0.62 1650 4.2 2.6 -1.6 0.6 0.5 0.2 1.3 1.0 0.2 -1.2 0.5 -0.9 1981 0.400 0.36 1623 0.2 2.6 1.7 1.8 -0.8 0.8 -0.9 -0.7 -0.9 -0.1 1.1 -0.5 1982 -0.342 -0.74 1605 -3.1 -2.2 -0.4 -3.4 1.2 -1.9 -0.2 -0.1 -0.2 0.6 -1.2 2.0 1983 -0.075 0.27 1594 3.8 2.1 0.3 -0.5 -1.7 0.6 0.5 1.9 0.7 0.6 0.6 -5.7 1984 -0.042 0.03 1592 -2.2 0.5 -1.6 1.1 0.7 0.7 -0.8 -1.0 -1.2 0.0 -0.8 5.0 1985 -0.400 -0.36 1594 -1.1 -3.4 2.2 2.3 1.2 -0.7 0.4 -1.2 0.1 -0.1 -0.4 -3.6 1986 0.517 0.92 1590 3.8 2.1 0.8 -0.6 -0.3 1.3 0.0 0.1 0.5 -0.1 0.3 3.1 1987 0.592 0.07 1589 -1.1 0.9 -1.1 0.1 0.9 0.1 0.0 0.4 0.3 -1.2 1.4 0.2 1988 0.133 -0.46 1598 -1.8 -1.9 -0.3 -0.8 -0.9 0.0 0.5 1.0 -0.2 0.1 -0.4 -0.8 1989 -0.300 -0.43 1597 3.4 -2.0 -0.1 0.2 -0.6 -1.0 -0.4 -1.3 -0.2 1.0 -0.5 -3.7 1990 0.750 1.05 1572 1.4 3.8 1.3 0.0 -0.6 0.8 -0.2 0.5 1.4 0.0 1.3 2.9 1991 0.608 -0.14 1549 -3.9 1.2 -0.4 0.5 2.1 -0.1 0.3 0.3 -1.0 0.2 -2.8 1.9 1992 0.075 -0.53 1536 2.0 0.0 0.0 -0.7 -1.2 -1.2 -1.3 -1.8 -0.3 -0.5 0.4 -1.8 1993 -0.458 -0.53 1529 -1.4 -4.5 -1.6 -1.0 0.2 0.2 0.8 1.4 -0.5 -0.4 -0.4 0.8 1994 0.183 0.64 1519 -1.4 0.4 1.5 1.5 -0.3 1.6 0.1 -0.4 0.9 0.7 1.9 1.2 1995 0.142 -0.04 1495 2.5 2.2 0.0 -1.6 -0.6 -1.3 0.3 1.4 -0.3 0.2 -1.4 -1.9 1996 -0.375 -0.52 1464 -1.9 -0.5 -2.6 0.2 0.7 0.6 -0.5 -1.2 -0.3 -0.5 -0.9 0.7 1997 -0.117 0.26 1431 0.2 0.9 2.9 -1.1 -1.0 -0.6 0.1 -0.3 1.2 0.1 0.7 0.0 1998 1.067 1.18 1428 2.8 1.4 -1.3 1.6 2.3 0.1 0.9 1.1 1.4 0.7 2.2 1.0 1999 0.733 -0.33 1447 -1.0 -0.1 0.0 0.3 -1.3 0.0 0.0 -0.4 -2.2 -0.6 1.4 -0.1 2000 0.308 -0.43 1429 -0.2 0.2 2.2 0.0 1.2 0.1 -0.8 0.3 0.3 0.5 -4.5 -4.4 2001 0.575 0.27 1434 -0.7 -2.7 -2.8 0.7 -0.2 0.0 0.3 0.2 -0.1 -0.6 4.4 4.7 2002 0.475 -0.10 1421 1.9 1.0 -0.4 0.0 -1.6 0.8 0.8 -0.4 1.1 -0.9 -2.6 -0.9 2003 0.250 -0.23 1411 -2.0 -1.9 1.6 -0.8 0.7 -1.4 -0.4 0.6 -1.2 1.7 0.4 0.0 2004 0.283 0.03 1382 -1.0 0.4 1.7 0.3 1.0 0.1 -1.0 -1.9 0.5 0.0 0.5 -0.2 2005 0.483 0.20 1214 1.5 2.1 -2.5 -0.1 -1.6 0.9 1.0 1.6 0.8 0.0 0.1 -1.4 2006 0.692 0.21 1177 3.6 -1.5 0.3 0.1 0.1 0.0 0.0 0.0 -0.2 -0.1 0.0 0.2 2007 0.458 -0.23 134 -3.3 -2.0 1.6 -2.1 0.4 0.0 -0.9 0.8 1.9 2.4 -0.1 -1.5 2008 -0.275 -0.73 136 -0.7 1.4 -2.2 0.2 -1.4 0.0 -0.1 -1.5 -0.8 -2.2 -0.6 -0.9 2009 -0.217 0.06 136 -0.7 1.1 0.5 0.0 0.8 -0.3 -0.7 -0.1 0.0 -0.8 1.6 -0.7 2010 -0.200 0.02 134 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 For Country Code 425 From input file data/EMS.inv11.M1800.dt
So my conclusion out of all this is that we have no net warming in the world. We have some rise out of the Little Ice Age and we had some net warmth during the warm phase of the PDO in the 1990’s (especially toward the end); but that is leaving now that we’re in the cold phase of the PDO.
Further, figuring out what is happening to the planet as a “global average temperature” has a major obstacle in the high volatility induced from extreme latitudes, their sensitivity to cyclical weather patterns (such as the PDO) and the unequal distribution of land on the globe with such a large part of it being very far north (and by extension, so little being very far south).
BTW, for folks who have asserted that GHCN is missing some thermometers due to the data not being made available “in real time” or from flakey foreign governments: I find it rather odd that here, in the USA data, we have the 2006 “Lesser Dying of Thermometers” showing up “big time”. And yet the same folks who make GHCN (that is NOAA / NCDC ) also make the USHCN data set that contains all those thermometers and their data…
So that whole ‘accident of creation’ story just doesn’t work for me… someone had to choose what went in to GHCN in 2005 and what did not go in in 2007. The data clearly were (and are) available to NCDC, since they already have them in the USHCN.
Canadian stations went to ASOS systems during the 90’s.
Hey, take it easy on that wall; that thing cost somebody some real money…
I really enjoy the blocks of numbers. It is so thoughtful of you to leave something for the rest of us to do, that merely involves some cut/paste/import/export work. This way we can all make a contribution to the analytical effort.
My eyesight is not what it was;
{A Recent Odometer Rollover party for me occurred when, for the first time in my so-called life, the grocery clerk took a second look at me and spontaneously applied a 5% ‘Senior Discount’ without commentary. Kind of like being carded for a sixpack but in reverse}
So probably I overlooked the explanation of ‘Heads’ and ‘Tails’.
I still have an active imagination, and I imagine it has nothing to do with Heads, and Shoulders, Knees and Toes…
Nor Coin Tossing, nor Tails of distributions,
But I cannot yet imagine a use of those two words that feels consistent with your other text.
Maybe you would be so kind as to publish More Data Blocks Please, including a series where a Big Head or Big Tail can be readily observed.
As far as demonstrating my ignatz, I didn’t comprehend where on those lovely graphs I should be looking for the grrooovy MOD flags. (or some effect of the MOD flag on the cut of the jib)…
Yikes I’ve set off the Runaway Analogy alarms, the meter is pegged at Brain Salad!
Run For It Men!
It’s Every Man For Himself!
RR
Gotta get more sleep…
I would be interested in your take on Tamino’s latest work. Is he right or wrong. Is his analysis fair or misleading?
So far I have disagreed with everything Tamino has posted (but my comments to his site never get through).
I don’t have the background to judge the accuracy of this latest effort and would love to see someone doing similiar work review or audit this piece:
REPLY:[ I’ve looked at it, despite having no interest in it. He leaps to the conclusion that the only way bias could have an impact is if the anomalies (done his mystery sauce way) are divergent. That is an error of assumption (see the “hypothetical cow” posting here).
https://chiefio.wordpress.com/2010/02/07/of-hypothetical-cows-and-real-program-accuracy/
GIStemp does “Basket A” to “different post processing and adjusting and infilled and UHI changed and near the end of the process … Basket B”. In that context, there are plenty of opportunities for bias to leak through. So he has assumed something that is not true, and proceeds to waste his time after that. “Given my conclusions what assumptions can I draw” comes to mind. Kind of a pointless effort and one that I don’t feel any need to waste my time explaining. -E. M. Smith ]
@Ruhroh:
“head” and “tail” are Unix / Linux commands that give you, for “head” the first lines of text in a file and for “tail” the last lines of text. So, for a file sorted from “low to high” the “head” command will give you the first, or lowest, values. The command “tail” would give you the last, or largest values. You can see that in the above tables of numbers as the month of temperatures in question being either very negative or very positive.
I’ve added some text to the posting to clarify that point.
The “modification flag” is the 12 digit of the “StationID field” (that is actually encoded with meaning). So the first digit is the “region” where, for example, “4” is North America and “5” is the Pacific Ocean Basin (with some emphasis toward the middle and south in that Hawaii is part of “4” while Japan is part of Asia so starts with a “2” region…) The next 2 digits give you the country in that region, so 403 is Canada and 425 is the USA. Those 3 digits taken together are often called the “Country Code”. You then get 5 digits of primary station ID then 3 digits of exact location (so you might have 12345 meaning “near the major reporting station on Hawaii near Hilo” followed by 3 digits for the particular thermometer location in question (i.e. 1 for “near the runway” and 2 for “near the tower” and 3 for “over behind the hangar”). So that’s 3+5+3 or 11 digits. The 12th digit is the “modification history flag” and tells you if it’s “Read by Mary in the Morning” or “read by Tom at night” or “the old glass thermometer” or “the new ASOS box”.
So the naive approach I’ve taken is just to average all readings that are available for one location for one time. This is based on eyeballing a lot of the records with duplicate Stn/Substation 11 digit values and overlapping in time. Sometimes there are a month or two that diverge, but almost all the time they are identical or nearly so. (Reasonable in that folks do try to get replacement equipment calibrated for a clean overlap / splice). And in the case where there is a divergence, which one would you choose, anyway? The only blanket rule that looks reasonable to me is “split the difference” and move on.
FWIW, the percentage reduction in the data from this combining is 26%. 74% of the data volume remains after averaging the various mod flags into one data series. So about 1/4 of all the ‘temperature data’ in GHCN is just duplication.
@RickA
Since I’ve observed a pattern of folks asking “What do you think of {foo’s} ‘proving’ {this or that broken attack} relative to what you have done” I’ve begun adding SPAM filter entries to dispose of them. I have no need to:
1) Advertise for them.
2) Waste my time on Yet Another Hypothetical Cow.
3) Waste everyone else’s time on such Cow Hunts.
4) Feed Trolls.
5) Provide “broken argument” fodder.
6) Support “Climate Stability Denier” graffiti and tagging.
etc.
I’m interested in the data, and what they say: not how other folks can distort them for an agenda. Be advised.
Substantially all of them make the same set of errors anyway. They use selected time periods that are inadequate. They over average things and lose the important bits (such as that Canada warms way early then stops until ASOS come on line, Pacific Basin does not warm). Or they compare two things that match at some past time with the implicit assertion that if something matched in the past it must match in the future; when it doesn’t work that way. (You can’t prove what ought to be there but is now missing had any particular shape or pattern by showing what it was in the past and hoping.) As the above Canada example shows, the regions have different responses to long cycle events, so I can get lots of match periods that will then break down later. Etc. So any and all postings of the form “What do you think of {what foo did}?” will hit the bit bucket and folks can just re-read this paragraph.
Think of this as a zero tolerance policy for trolling.
Frankly, given how badly “Temperature Realists” have been treated at those “Global Stability Denier” sites, it’s the least I could do to be fair…
Mr. Watts has suggested (a couple of times today) that I address my quetions to you. Here’s a C&P of a post I made on WUWT along with Mr. Watt’s reply. WOuld you be so kind as to respond?
So, can you answer the following question?
Have you or any one you know performed a statistical analysis that contradicts Tamino’s claim:
[ rest of cross post snipped as redundant ]
REPLY:[ I’ve looked at it, despite having no interest in it. He leaps to the conclusion that the only way bias could have an impact is if the anomalies (done his mystery sauce way) are divergent. That is an error of assumption (see the “hypothetical cow” posting here).
https://chiefio.wordpress.com/2010/02/07/of-hypothetical-cows-and-real-program-accuracy/
GIStemp does “Basket A” to “different post processing and adjusting and infilled and UHI changed and near the end of the process … Basket B”. In that context, there are plenty of opportunities for bias to leak through. So he has assumed something that is not true, and proceeds to waste his time after that. “Given my conclusions what assumptions can I draw” comes to mind. Kind of a pointless effort and one that I don’t feel any need to waste my time explaining. -E. M. Smith ]
E.M., I’ve just finished an analysis/posting on how the calculation of annual mean can be affected by missing months. It has been quite a journey. I started out intending just to look at the quality and quantity of missing data, but as usual couldn’t help playing with the data.
I’ve worked though the anomaly calculation for several stations (posted about 2, but the others agreed) and how it is used to back-calculate annual means when there are missing months. The results are interesting – the annual anomaly can be affected substantially for stations that have a large variation in temperature (cooler ones). I have not taken this as far as is possible yet on the blog but I think you’ll get the implications.
I only had a quick glance earlier at what you’ve posted above – promise I’ll come back when fresh tomorrow.
REPLY: { You mean this one:
http://diggingintheclay.blogspot.com/2010/02/of-missing-temperatures-and-filled-in.html
I’ll take a look at it. Yes, it’s a very interesting problem. One I’ve tried to tackle with the dT/dt code as evidenced in the report above. Such a rats nest of ‘issues’ data dropout has. And yet folks continue to think that they can not the temp average to 1/100 C despite it all(!).
It will be sheer joy to read. I’ve just sunk about 2 hours into a “thread war” over on WUWT about something that interests me not at all. (Yet another “Hypothetical Cow” story that folks INSIST I must address and prove wrong. You could spend your whole life trying to fix all the things folks can get wrong. So I refuse.) I’d much rather learn some new stuff from what work you’ve done. If I’m lucky it could even help me with the final tuning of dT/dt.
-E.M.Smith ]
Hi
Not wanting to complicate your life, but it does look like Canada Met Office puts its data online (presumably though not guarranteed before it is “adjusted”, and it might be interesting to compared with this for your odd stations.
The webpage with the links of what they have online is here:
http://www.climate.weatheroffice.gc.ca/prods_servs/index_e.html
And the data for download here
ftp://arcdm20.tor.ec.gc.ca/pub/dist/CDCD/
As the Arctic region has been one of the “hotting up” regions, it would be good to understand whether the heat is in the raw record or in the subsequent adjustments
REPLY: [ Always glad to have links to various BOM sites that make data available. Haven’t had the time to follow up on them yet, though. One of my “someday do” list items is to make A/B reports of the various station data for what are supposed to be the same place. -E. M. Smith ]
A bit more info on what is available is here:
http://www.climate.weatheroffice.gc.ca/FAQ_e.html
Margaret
I have been looking at the Canadian data (as shown in the another thread on this site) and I can tell you that the data on the Enivornment Canada (EC) site in your first link in your top post is adjusted data by EC. I just did a GISS/NCDC/EC Pepsi Challenge style post on my site (click on my name if you want to head over). In that post I show the “raw” data for 2 different Canadian stations that EC reported to NCDC via CLIMAT report. When you compare the data from the EC site to the Raw and Adj GHCN data files you can see that the EC data is adjusted. I haven’t looked at whats in that zip file yet but thanks for posting it! Now off to take that file apart.
REPLY: [ Ah, I see Boballab is doing that bit of work. Here:
http://boballab.wordpress.com/2010/02/25/temperature-analysis-its-climate-sciences-own-pepsi-challenge
Isn’t Science Barn Raising fun ;-) And folks wonder why I like putting time into this part instead of figuring out what some other folks got wrong… Discovery, gotta love it! Guess I’ve got two sites to visit before I retire tonight… OK, I got sucked into reading it directly. nice. Very nice. – E. M. Smith ]
Aye, there’s the rub. Who decided to select the station data? Yargh! Keel-haul the… [snip if you want. I couldn’t resist]
Came over looking for tip jar.Thanks for your fine work.Environment Canada is suspect,they use “environment canada’s science”,whatever that is.But I have written confirmation from them, that they base their faith in global warming on the IPCC.No answer as to who signed off on the acceptance of IPCC FAR on behalf of canada.Nor any answer as to any internal analysis for climate trends in canada.Apparently the individual station records are accurate.Any idea on how to check?Would the local weatherstation keep hard copy records?Its funny,I trusted the competence of these agencies until I started looking into Al Gores claims. After writing to the minister in charge I now see incompetence and govt think all arround me.Sorry off topic,I think what you are doing is vital to my freedom so thank you.
The Zip file is big and the reason for it is that it’s daily readings. This is from the Readme file:
My compliments on your sleuthing.
Regarding “clues”, lets see, can “thermometers” detect:
* Temperature changes from Chinook winds? e.g., -48°C (-56°F) to 9°C (49°F).
* Northern lights? via Galactic Cosmic Rays?
* Mining Oil Sands or petroleum processing?
* Caribou herds? or
* Ice jams?
Might have to ask the Mounties about Technological crime! Happy hunting!
On clues, a friend in Vanuatu said the he knew the wind had gotten over 250 km/h as that was when the anemometer blew away! You might also have to consider Arctic cyclones.
(PS his rule of thumb was 7 bolts per square meter shuld keep the siding on!)
Looks like the ASOS of some eras have known heating bias from a known mode of failure. A retrofit kit was made available in the USA. Wonder if anyone knows for Canada?
http://wattsupwiththat.com/2008/01/10/inside-the-asos-ho83-tempdewpoint-sensor/
And this graph too:
And this article
http://ams.allenpress.com/perlserv/?request=get-document&doi=10.1175%2FJTECH1752.1&ct=1
Says:
At the Asheville site, the ASOS and USCRN siting difference led to ΔTlocal effect of about 0.25°C, which is larger than that of ΔTshield effect. This site-specific warming, believed to be caused by the heat from the airport runway and parking lots next to the ASOS site, was found to be strongly modulated by wind direction, solar radiation, and cloud type and height.
So we can have 1/4 C of airport heat island effect for those (an increasing percentage) at airports…
boballub
Thanks for pointing me to your site. I am so pleased that multiple people are spreading the fun of sorting out what is going on.
It is easy to get overwhelmed, I guess. The modification flags seem to be the crucial thing in the beginning of your posting, but you said very little about them.
1. How many different modification flags (ModFlag) are there? Are they dated?
2. What do they mean?
3. How many occurrences of each?
4. How many, say, in Canada or Russia, or at a certain elevation (elev), or latitude (lat)?
5. How many flags, by time (t)?
6. How many flags of the different kinds by t?
7. How does dT/dt vary with these flags? That is, what is dT/dt before them respectively after them.
When arrived at 7. a statistician would, I believe, accept the dT/dt as the right variable to study (perhaps also consider log(T(t)/T(t-1)). Thereafter he would run a regression on that dT/dt, letting the station be the observation and starting with
dT/dt = constant + b*t + c*long + d*lat + e*elev + f*ModFlag
where f and ModFlag are vectors of the right dimension (=number of different modification flags in the data)
All these variables are dummies, and the most complicated one is ModFlag which is (for each type of flag) =0 before the t of modification, and =1 afterwards.
The estimated parameter vectors f would tell the effect of the modification on dT/dt. Thereafter you could focus your study towards the flags which have an interesting effect, and the risk of becoming overwhelmed would decrease.
But then again, this might be the wrong direction to go. Perhaps you should keep your eyes on the death of the stations, and I suspect there is no ModFlag for that, is there? So that would be the first thing to do, i.e. adding that flag as exactly dated as possible. And the cause of death, if anything is known. Mortality is the direction to go!? And thereafter the consequences of mortality (another complete field of study).
I believe your work is very important.
Best of luck and lots of fun!
Kari
Can’t comment on sleuthing the data errors or tricks, but, my, oh, my, you are putting Sherlock Holms to shame simply by providing the questions (and most of the data) with which to inquire. I like your frequent offers to: “Check me out on this, and I will be checking myself more than once.” Way to go.
What a model for young (and older) scientists, coumputer whizzes, and, I think, mathematicians. Once this bunch of incompetents infesting “public service” is swept from their lucrative perches, there should be little “government debt”. Western and other developed societies can become insolvent again. Oh, yes, there still remains that small matter of developing our own efficient energy resources so our dollars or other marks of affluence can stay at home.
Again, much gratitude.
REPLY: [ Just doing that Joe Friday thing: “Just the facts, maam, just the facts.” Perhaps with a bit of Columbo mixed in: “I see… Thank you Mr. Hansen. The coffee was down this way? Thanks again…. Oh, just one more thing, yesterday you said… and that doesn’t quite add up… ”
And on the energy issue. Frankly, that’s one of my major motivations in all this AGW clap trap. The AGW thesis would have us abandon the best, fastest, and most well proven path to energy independence. Right Now we could do what South Africa has done and build coal to liquids plants making gasoline and Diesel. We could be done in 5 to 10 years Then have about 300 years of self sufficiency while we worked out electric cars and biofuels from algae et all. But no. We’re supposed to stay “stuck on oil” for the 20 years it would take to convert everything to electricity (from where does that electricity come without coal?) or die trying. Just stupid. So if you look in the article about “no shortage of energy” you will find lots of ways to be energy independent. I just wish our “Leaders” would put 1/10 the money into them that is being squandered on the AGW nonsense. see:
https://chiefio.wordpress.com/2009/03/20/there-is-no-energy-shortage/
-E.M.Smith ]
@ Kari Lantto
The modification flags seem to be the crucial thing in the beginning of your posting, but you said very little about them.
They indicate when there is a difference in how the data ought to be handled. So a single thermometer might be read by Mary at noon each day and by Bob at 8 AM and 8 PM. They would need two different TOBS adjustments, so they would each be flagged with a different “Modification History” flag (even before that modification is applied). Similarly, an automated ASOS box would require different “adjustments and corrections” than a LIG Liquid In Glass thermometer in a Stevenson Screen. So a different “Modification History” flag gets assigned. It’s just a way to keep straight what readings where made in what way from which gear.
1. How many different modification flags (ModFlag) are there? Are they dated?
By definition, all readings have a modification flag. Most of them have a ‘0’ meaning “the fist thermometer installed here read in the usual way”. The maximum at present would be 10 (numbered 0-9) as then the field would be used up. (I presume when that happens they could change the field from numeric to alpha or some such, but I’d also expect a short term patch to be incrementing the substation field by one).
Each record is a StationID (Country Code, Station, Substation) for 11 digits, then the mod flag for 1 digit. While most of the substations are 000 (meaning “I am the major site”) some have a 1 or a 2 or… (meaning “I am near the major site”). If you used mod flag 9, I could see a choice of “Rewrite all the code? Nah, just make it substation 1.” as most records have not substation values used.
2. What do they mean?
There is something in the readings that ought to be modified differently from the other readings at this spot. It could be Time Of Observation TOBS or different equipment or… It basically just says this is a different data series and might need a different set of “modifications” done to it to “correct” things… There is no specific meaning for any particular number. It’s just an incrementally assigned “something changed” flag.
3. How many occurrences of each?
Highly variable. The world starts with “0” all over, then over time things change. Those places doing R&D change the most. Those at historical sites change the least. There was a bit bolus of them with the change from LIG to ASOS, for example, at airports.
4. How many, say, in Canada or Russia, or at a certain elevation (elev), or latitude (lat)?
More in the flat land than on mountain tops (airports changed to ASOS but your local observatory likes old classical equipment…). “Advanced” countries took more as they converted to electronic gear first (and often several times working out bugs…) while poor countries have fewer (typically) as they didn’t buy new gear, just read the old LIG thermometer that was hanging on the shed for the last 50 years…
5. How many flags, by time (t)?
Starts with all zeros, increases over time a little. Mostly a big bolus with the electronics roll out like ASOS (and it’s sub types). Generally stabilized a bit after 2000.
6. How many flags of the different kinds by t?
There aren’t really ‘different kinds’ of mod flag. It’s just an incremental “Something changed” counter. For each ‘something changed’ you need to investigate to find out ‘what kind of change’.
7. How does dT/dt vary with these flags? That is, what is dT/dt before them respectively after them.
The first version of dT/dt just processed all records the same. This gave a site with 4 overlapping records with different mod flags 4 votes. This showed up (and was the cause of this posting / investigation) when I found some of those Canadian sites were voting for a +20 jump in one month, and doing it 4 times !!
That’s why I had to choose what to do with the mod flags. I chose to just average the time series together. Basically, to splice them all together via averaging (but they only get one vote for one place). The error from 4 x 20 votes was greater than the error from (20 x 4)/4 even if one of them was 20.1 and another was 19.9). So that’s why the “mod flag combing code’ was done.
Now dT/dt will simply respond to the whole temperature series as one big thermometer record. I accept the potential for a ‘splice bias’ and watch for it as an observation at the end rather than a correction hidden up front.
So dT/dt varies directly with the temperature reported (bias and all). So, for example, the ASOS gear has a couple of known warm biases. (It’s warm air exhaust from the electronics can get sucked back up it’s skirt and the ‘shelter’ is thin so a solar heating bias exists compared to the thicker shelters). If I combine a historical Liquid In Glass thermometer in a Stevenson Screen with mod flag 0 along with an ASOS, you get a minor hockey stick. And I think that is what you see in those Canadian graphs from GISS up top. The last segment is higher than all the earlier data. Not because the earth warmed, but because the equipment and procedures changed. The dT/dt code finds the same thing.
There are a whole bucket of “adjustments” that are supposed to fix that, but from what I’ve seen, they rarely do (and they often make things worse). So I’ve chosen to take the path of leaving the data lone and looking at what it says. And if I find a bunch of Canadian Hockey Sticks that all ‘lift off’ right at the time where the ASOS gear is installed, that’s an answer not something to be adjusted away…
Your proposed statistical analysis is interesting. I’d be interested in doing it, were I not already up to my eyes in more stuff than I can get done already. But yes, I think there is a major issue around different trends by mod flag and offsets by mod flag (such as that ASOS bias points toward) and somebody ought to “Dig Here!”. But for me it’s still a bit of a ways off in the future.
Oh, and by definition there is no mod flag for End Of Life of a thermometer. EOL is just assumed to be the last record reported for that stationID. (Yet we know from the Zombie Thermometers that that is not true… stations can come back months, or even years later and begin to report again…)
One of my major complaints about the data structure is that the Meta Data are not kept ‘by time’ but only anchored to “now”. So Berlin Templehoff is an airport (has an “airstation’ flag in the data) and that marks it as an airport in the 1700’s too. Clearly nutty. Yet it is decommissioned and some day the data will be updated to “not an airport” – then all those airport years from the birth of aviation to the Berlin Air Lift and the growth of Luftansa will just disappear into the “not an airport” flag… All the metadata is based on “now”.
So a much better data structure would have metadata based on time. Then the various changes over time could just be tracked in that metadata, including the things that the mod flag sort of tracks now, and including the installation, update, and end of life of a given instrument. But it isn’t done that way now.
A great post Mr. Smith. Lucid, informative, and thought provoking.
As someone who grew up in Minnesota and N. Dakota near the Canadian border during the 60s and 70s, the issues of cold weather and warming have always been near and dear to my heart. With this background, I noticed in your chart of October “heads” the following US stations in Minnesota and S. Dakota:
4257275500501925 30 -38 -8 23 13 3 -13 13 10 -128 -5 0 FOSSTON 47.57 -95.73 399 398R -9FLxxno-9x-9WARM MIXED B
4257275000101925 30 -34 -4 25 8 -6 -12 6 9 -116 -20 36 PINE RIVER DAM 46.67 -94.12 381 375R -9FLxxLA-9x-9COOL FIELD/WOODSB
4257264400101925 36 7 36 41 23 10 8 6 47 -114 -6 41 ALBERT LEA 3SE 43.62 -93.42 374 383S 18FLxxLA-9x-9COOL CROPS B
4257265300401925 34 9 55 54 35 30 14 39 47 -113 -15 48 GANN VALLEY 4NW 44.07 -99.07 524 515R -9FLxxno-9x-9COOL CROPS A
4257265400401925 14 -6 44 43 27 10 0 22 36 -113 -4 45 WATERTOWN FAA AP 44.92 -97.15 532 529S 18FLxxLA-9A 2COOL CROPS C
All of them are from 1925, which make sense, because they are relatively close together and all in a similar geographic area. Although not in the “high latitudes” like most of the stations that you found with high variability, their latitude is “somewhat high.” They are far removed from the moderating oceans, and have great annual variability in temperature.
If Fred’s comment about the adoption of AWOS in Canada in the 90s doesn’t solve the conundrum to your satisfaction, perhaps looking at subsets of the data by latitude might reveal something interesting. An example might be to do the same kind of analysis you have done here, comparing the northern half of inland Canada with the southern inland half, and the far northern portion of inland US (say the 45 to 49 latitude, or a proxy like ND, SD, and MN). Or, since most of the Canadian population lives near the border with the US, you might divide the Canadian subsets into 3rds or 4ths. Like Canada, most of the northern tier of the US is sparsely populated, so there should be a better chance of avoiding UHI effect than in a set of the whole US.
I know from personal experience that the winters in ND, SD, and MN were warmer in the 80s, 90s, and 2000s than they were in the 60s and 70s. But I wouldn’t be surprised to see that the warmest years were in the 30s. I remember learning that the record high temperatures in ND occurred in the 30s. I don’t think those records have been eclipsed.
I’d try it myself, but unfortunately I don’t have a math/engineering/computer background to give me tools or experience working with large data sets.
It’s interesting to compare your conclusion that while there is “no net warming in the world,” the data shows Canada comes up warming, with the data analysis findings of Eugene Zeien. Looking at raw temperature data, he found a warming trend only in the far northern latitudes: +75 to +79.99.
http://justdata.wordpress.com/2009/12/28/step-by-step-debunking-climate-change/
and this chart: http://justdata.files.wordpress.com/2009/12/rawtempbylatitude_23.jpg
Most of the stations you highlight are at lower latitudes (50s and 60s). Eyeballing his graph, it looks like the latitude range of +65 to +74.99 might show a warming trend of around 2 degrees from 1910 to current, which would be similar to what your summary chart for Canada shows.
If you did a summary chart for similar latitudes in Siberia, would you get results similar to Canada? Or perhaps it would show some cooling that would tend to balance out the warming in Canada.
Quote from the Civil War about an officer not known for his veracity. (The book is “Campaigning With Grant” by Horace Porter)
“It got so bad you couldn’t even believe the opposite of what he said”
Thanks Chiefio!
You have ably demonstrated that if you want to track trends from baseline, first you need to be sure of your baseline. And the one in question is kind of… off base…